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+---
+created: 2026-04-10T10:00+08:00
+updated: 2026-04-12T22:30+08:00
+author: GitHub Copilot
+source_pr: 2263
+source_branch: YKDZ/FedML:exp/n50-fedavg-baseline-20260411
+status: archived-spec
+---
+
+# N=50 FedAvg 无攻击基线实验设计 v2.1
+
+> 本文为从 PR `#2263` 整理入库后的归档版本；对应结果归档见 `doc_fedavg/n50_results/`，对应验收报告见 `doc_fedavg/28_[2026-04-12]_N50_FedAvg_实验结果与验收归档说明.md`。
+
+## §0 文档定位
+
+| 属性 | 值 |
+|:-----|:---|
+| 类型 | 平台级基线实验设计（非防御规格） |
+| 状态 | **v2.1** — 入库归档版（来源 PR #2263） |
+| 前置依赖 | V2.1 冻结参数（plans/22）、N=10 M1 基线（reports/03） |
+| 后续消费方 | 全部 8 条防御基线复现（Krum § 7、Trimmed Mean § 7 …）、全部攻击实验、论文 Table 1 |
+| 设计哲学 | 单一确定性方案，无可选项 |
+
+### 背景
+
+V2.1 将 $N$ 从 10 提升至 50 后，仅在 SA 攻击实验的控制组中获得两个零星 FedAvg 数据点（CIFAR-10 α=0.5 seed=0: 73.31%；MNIST α=0.5 seed=0: 96.78%@T=50）。缺少系统性的 N=50 无攻击基线数据，导致：
+
+1. **防御基线无锚** — Krum/Trimmed Mean 的 AC-C-3（clean acc 范围）只能借用 N=10 数据外推，缺少 ground truth
+2. **攻击实验无对照** — SA/LF 的 ASR/acc 指标无法与同条件 clean 精度对比
+3. **论文 Table 1 不完整** — 社区惯例要求每个 (dataset × α) 组合提供 "No Attack + FedAvg" 行
+
+本文档补齐这一缺口，产出 **30 组覆盖全矩阵的 N=50 FedAvg 基线数据**（2 数据集 × 3 α × 5 seeds），作为项目永久参考锚点。
+
+---
+
+## §1 社区校准锚点
+
+下表汇总三篇核心参考论文的 FedAvg clean accuracy，用于设定合理验收区间。
+
+| 论文 | 数据集 | 模型 | N | Non-IID | T | E/Iter | lr | FedAvg Acc |
+|:-----|:-------|:-----|:--:|:--------|:--:|:-------|:---|:-----------|
+| FLTrust (Cao 2022) | MNIST | CNN | 100 | q=0.5 | 2000 | iter=1 | 3e-4 | 96% |
+| FLTrust | CIFAR-10 | ResNet-20 | 100 | q=0.5 | 1500 | iter=1 | 2e-4 | 84% |
+| Fang 2025 | MNIST | CNN | 100 | h=0.5 | 2000 | iter=1 | 3.1e-4 | 95% |
+| Fang 2025 | CIFAR-10 | **ResNet-18** | 100 | h=0.5 | 1000 | iter=1 | 0.005 | 78% |
+| FLAD (Tang 2025) | MNIST | CNN | **50** | q=0.8 | **20** | — | — | 94.9% |
+| FLAD | CIFAR-10 | **ResNet-18** | **50** | q=0.1 (≈IID) | **20** | — | — | 68.8% |
+| FLAD | CIFAR-10 | **ResNet-18** | **50** | q=0.5 (non-IID) | **20** | — | — | 64.8% |
+
+**校准要点**：
+
+1. FLAD 是唯一 N=50 数据源，但仅 T=20（严重欠训练），其数值应视为各条件下的**精度下界**
+2. FLTrust/Fang 的 CIFAR-10 结果（78–84%）基于 N=100 + T≥1000 + 更低 lr，作为**理论上界**参考
+3. 我们的 N=10 M1 基线是最直接可比的内部数据，N=10→N=50 导致每客户端数据从 ~5000 降至 ~1000（CIFAR-10），预期精度下降 3–8pp
+
+### Dirichlet α 与 q-based 分区的近似映射
+
+q-based 分区中，$q$ 表示客户端中来自主导类的数据比例（10 类时 $q=1/10=0.1$ 即 IID）。
+
+| 我们的 Dirichlet α | 近似对应 q 值 | 含义 |
+|:--:|:--|:--|
+| 0.1 | q ≈ 0.7–0.8 | 极端 non-IID |
+| 0.5 | q ≈ 0.5 | 中度 non-IID |
+| 100 | q ≈ 0.1 | 近 IID |
+
+---
+
+## §2 冻结参数配方
+
+所有参数沿用 V2.1 冻结版（plans/22），无任何修改。
+
+### 全局固定参数
+
+| 参数 | 值 | 说明 |
+|:-----|:---|:-----|
+| `training_type` | cross_silo | 跨机构模拟模式 |
+| `federated_optimizer` | FedAvg | 联邦聚合策略 |
+| `client_optimizer` | SGD | 本地优化器 |
+| `momentum` | 0.9 | 客户端 SGD 动量（PyTorch/ResNet 社区默认值） |
+| `server_momentum` | 0.0 | 服务端动量（= pure FedAvg，VeriFL Phase 3 恒等变换） |
+| `server_lr` | 1.0 | 服务端学习率（= pure FedAvg） |
+| `batch_size` | 64 | 本地 mini-batch 大小 |
+| `partition_method` | hetero (Dirichlet) | 数据分区方式 |
+| `client_num_in_total` | 50 | 总客户端数 |
+| `client_num_per_round` | 50 | 每轮参与数（全参与） |
+| `attack` | **none** | 无攻击 |
+| `defense` | **none** | 无防御 |
+| `pmr` | **0.0** | 恶意客户端比例 = 0 |
+
+### 任务线差异参数
+
+| 参数 | CIFAR-10 + ResNet-18 | MNIST + LeNet-5 |
+|:-----|:---------------------|:----------------|
+| `learning_rate` | 0.01 | 0.01 |
+| `weight_decay` | 1e-4 | 0.0 |
+| `comm_round` (T) | 100 | 100 |
+| `epochs` (E) | 1 | 1 |
+| α 集合 | {0.1, 0.5, 100} | {0.1, 0.5, 100} |
+| Seeds | **{0, 1, 2, 3, 4}** | {0, 1, 2, 3, 4} |
+
+> **Seeds 统一为 5 seeds 的理由**：V2.1 原方案 CIFAR-10 用 3 seeds 是基于"训练成本较高"的工程便利性考量。但作为永久沿用的基线数据，3 seeds 的统计置信度不足（尤其 α=0.1 的 N=10 基线 std=10.93%），且 Fang 2020 使用了 50 次重复。论文 Table 1 中两个数据集统一报告 "mean ± std (5 seeds)" 格式更规范。增加的 6 组 CIFAR-10 实验（每组 ~15 min）是一次性投入，收益覆盖全部后续防御/攻击实验。
+
+### 训练量等价性
+
+社区论文通常使用 $R_l = 1$（每轮单个 mini-batch）+ 大 $T$（1000–2000 轮），而我们使用 $E = 1$ epoch + $T = 100$。两者的总训练量对比：
+
+| 方案 | 每轮梯度步数 | T | 总梯度步数 |
+|:-----|:-----------|:--:|:----------|
+| FLTrust CIFAR-10 | 1 | 1500 | 1,500 |
+| Fang CIFAR-10 | 1 | 1000 | 1,000 |
+| **我们 CIFAR-10** | **~16**（1000 样本 / batch 64） | **100** | **~1,600** |
+| **我们 MNIST** | **~19**（1200 样本 / batch 64） | **100** | **~1,900** |
+
+我们的总梯度步数（~1,600）**不低于**社区论文的典型值（1,000–1,500）。$T = 100$ 视觉上较小，是因为每轮训练更深（整个 epoch vs. 单步），本质等价。
+
+---
+
+## §3 实验矩阵
+
+### 3.1 矩阵总览
+
+| 组号 | 数据集 | 模型 | α | Seed | T | 实验数 |
+|:----:|:-------|:-----|:--:|:----:|:--:|:------:|
+| C1–C5 | CIFAR-10 | ResNet-18 | 0.1 | {0,1,2,3,4} | 100 | 5 |
+| C6–C10 | CIFAR-10 | ResNet-18 | 0.5 | {0,1,2,3,4} | 100 | 5 |
+| C11–C15 | CIFAR-10 | ResNet-18 | 100 | {0,1,2,3,4} | 100 | 5 |
+| M1–M5 | MNIST | LeNet-5 | 0.1 | {0,1,2,3,4} | 100 | 5 |
+| M6–M10 | MNIST | LeNet-5 | 0.5 | {0,1,2,3,4} | 100 | 5 |
+| M11–M15 | MNIST | LeNet-5 | 100 | {0,1,2,3,4} | 100 | 5 |
+| | | | | | **总计** | **30** |
+
+两个数据集均为 3α × 5seeds = 15 组，格式完全对称。
+
+### 3.2 详细实验编号
+
+#### CIFAR-10（15 组）
+
+| ID | α | Seed | 备注 |
+|:---|:--:|:----:|:-----|
+| C1 | 0.1 | 0 | |
+| C2 | 0.1 | 1 | |
+| C3 | 0.1 | 2 | |
+| C4 | 0.1 | 3 | |
+| C5 | 0.1 | 4 | |
+| C6 | 0.5 | 0 | **校验锚**：预期 ≈ 73.31%（N50 SA 控制组已有数据） |
+| C7 | 0.5 | 1 | |
+| C8 | 0.5 | 2 | |
+| C9 | 0.5 | 3 | |
+| C10 | 0.5 | 4 | |
+| C11 | 100 | 0 | |
+| C12 | 100 | 1 | |
+| C13 | 100 | 2 | |
+| C14 | 100 | 3 | |
+| C15 | 100 | 4 | |
+
+#### MNIST（15 组）
+
+| ID | α | Seed | 备注 |
+|:---|:--:|:----:|:-----|
+| M1 | 0.1 | 0 | |
+| M2 | 0.1 | 1 | |
+| M3 | 0.1 | 2 | |
+| M4 | 0.1 | 3 | |
+| M5 | 0.1 | 4 | |
+| M6 | 0.5 | 0 | |
+| M7 | 0.5 | 1 | |
+| M8 | 0.5 | 2 | |
+| M9 | 0.5 | 3 | |
+| M10 | 0.5 | 4 | |
+| M11 | 100 | 0 | |
+| M12 | 100 | 1 | |
+| M13 | 100 | 2 | |
+| M14 | 100 | 3 | |
+| M15 | 100 | 4 | |
+
+### 3.3 已有数据审计
+
+| 来源 | 数据集 | α | Seed | Acc | T | 可复用？ | 理由 |
+|:-----|:-------|:--:|:----:|:---:|:--:|:--------:|:-----|
+| N50 SA 控制组 | CIFAR-10 | 0.5 | 0 | 73.31% | 100 | ✅ 用作校验锚 | V2.1 参数完全一致 |
+| N50 SA 控制组 | MNIST | 0.5 | 0 | 96.78% | **50** | ❌ 不复用 | T=50 ≠ V2.1 冻结 T=100 |
+
+**决策**：全部 30 组实验重新执行，确保一致性。C6（CIFAR-10 α=0.5 seed=0）的结果与已有 73.31% 交叉校验，偏差应 < 0.5%（种子确定性保证）。
+
+---
+
+## §4 验收标准 (AC)
+
+### AC-0：工程健康度
+
+| AC | 条件 | 判定标准 |
+|:---|:-----|:---------|
+| AC-0-1 | 无崩溃 | 30 组实验全程无 OOM / RuntimeError / 段错误 |
+| AC-0-2 | 日志完整 | 每组实验产出完整的 round-level JSONL，含 `test_acc`、`test_loss` 字段 |
+| AC-0-3 | 种子确定性 | C6（CIFAR-10 α=0.5 seed=0）结果与已有锚点 73.31% 偏差 < 0.5%（验证实验平台种子控制未退化） |
+
+### AC-1：精度范围
+
+基于 N=10 M1 基线（下调 3–8pp 因 N=50 数据稀释）+ 社区锚点推导。
+
+#### CIFAR-10 + ResNet-18
+
+| α | N=10 参考 | 社区参考 | **验收区间** | NaN 容忍 | 说明 |
+|:--:|:----------|:---------|:-------------|:--------:|:-----|
+| 0.1 | 56.43% ± 10.93% | 社区无 N=50 极端 non-IID CIFAR-10 直接可比数据 | **[35%, 65%]** | ≤ 1/5 seeds | 极端 non-IID 已知难例；N=10 时 5 seeds std=10.9%，N=50 数据更稀疏 |
+| 0.5 | 78.75% ± 0.45% | FLAD q=0.5 T=20: 64.8%（欠训练下界）; Fang N=100 ResNet-18: 78% | **[65%, 80%]** | 0 | N=50 已测锚点 73.31% 落入区间 |
+| 100 | 81.71% ± 0.38% | FLAD q=0.1 (≈IID) T=20: 68.8%（欠训练下界）; FLTrust ResNet-20: 84%; Fang ResNet-18: 78% | **[73%, 85%]** | 0 | 近 IID，N=50 数据稀释效应较小 |
+
+#### MNIST + LeNet-5
+
+| α | N=10 参考 | 社区参考 | **验收区间** | NaN 容忍 | 说明 |
+|:--:|:----------|:---------|:-------------|:--------:|:-----|
+| 0.1 | 97.55% ± 0.49% | FLAD q=0.8 T=20: 94.9% | **[90%, 98.5%]** | 0 | MNIST 对分区鲁棒，但 α=0.1 极端情况保留下界 |
+| 0.5 | 98.66% ± 0.28% | FLTrust: 96%; Fang: 95% | **[94%, 99.5%]** | 0 | N=50 已测锚点 96.78%@T=50，T=100 应更高 |
+| 100 | 99.04% ± 0.04% | FLTrust q≈IID: 96%; Fang: 95% | **[96%, 99.5%]** | 0 | 近 IID + 简单任务，期望高精度 |
+
+### AC-2：统计一致性
+
+| AC | 条件 | 判定标准 |
+|:---|:-----|:---------|
+| AC-2-1 | 单调性（α 维度） | 每个数据集内：mean(α=100) > mean(α=0.5) > mean(α=0.1) |
+| AC-2-2 | 单调性（数据集维度） | 相同 α 下：mean(MNIST) > mean(CIFAR-10) |
+| AC-2-3 | 种子方差合理性 | CIFAR-10 α≥0.5 的 std < 3pp；MNIST 全部 α 的 std < 2pp |
+| AC-2-4 | 收敛性 | 单组实验最后 10 轮 test_acc 的标准差 < 1pp（不出现末期震荡） |
+
+### AC-3：NaN 处理规则
+
+> **关键说明**：N50 SA 攻击实验中 CIFAR-10 α=0.1 出现 2/3 NaN 的根因是 Scaling Attack 的 auto-γ 缩放对 BN `running_var` 的极端放大（V2.1 §2.5）。**本实验为无攻击基线（PMR=0.0），不存在 γ 缩放机制**，因此 NaN 风险远低于攻击实验。但极端 non-IID + 数据稀疏（每客户端 ~1000 样本中可能仅含 1–2 类）仍可能导致训练不稳定。
+
+| 条件 | 处理方式 |
+|:-----|:---------|
+| CIFAR-10 α=0.1 出现 NaN（≤ 1/5 seeds） | 判定为 **WARN**——标注并排查原因（可能是数据分区极端不均导致），用有效 seeds 计算均值，不阻断后续实验 |
+| CIFAR-10 α=0.1 出现 NaN（> 1/5 seeds） | 判定为 **FAIL**——无攻击下多数 seeds NaN 指向基础设施问题（与 SA 的 γ 缩放 NaN 机理不同） |
+| CIFAR-10 α≥0.5 出现 NaN | 判定为 **FAIL**，排查基础设施问题 |
+| MNIST 任何 α 出现 NaN | 判定为 **FAIL**，排查基础设施问题 |
+
+---
+
+## §5 执行规格
+
+### 5.1 执行环境要求
+
+| 约束项 | 要求 | 理由 |
+|:-------|:-----|:-----|
+| GPU 型号 | **全部 RTX 4090**，不混用其他型号 | 同型号 GPU 保证 `deterministic_algorithms` 下 bit-exact 可复现；不同型号因指令集/精度路径差异可能引入微小偏差 |
+| GPU 配置 | **按数据集固定**（见下方标准配置） | 数值结果不受 GPU 数量影响，但训练耗时正相关。固定配置确保基线与后续防御/攻击实验的**时间可比性** |
+| `runtime_mode` | `single-gpu-deterministic`（`--gpu` 标志自动设置） | 启用 `torch.use_deterministic_algorithms(True)` + `CUBLAS_WORKSPACE_CONFIG` + `cudnn.deterministic` |
+| `enforce_determinism` | `true` | 控制种子初始化和确定性算法开关 |
+| `sort_client_updates` | `true` | 保证聚合顺序与 GPU 调度无关 |
+| `cpu_transfer` | `true`（`--gpu` 标志自动设置） | MPI 通信走 CPU tensor，避免跨 GPU 通信不确定性 |
+
+> **标准 GPU 配置**（与工程同学 `batch_n50.sh` 一致）：
+>
+> | 数据集 | GPU 数量 | 进程分配 (server + clients) | 说明 |
+> |:-------|:---------|:---------------------------|:-----|
+> | CIFAR-10 | 3 块 RTX 4090 | 13 + 19 + 19 = 51 | ResNet-18 显存需求较高 |
+> | MNIST | 2 块 RTX 4090 | 26 + 25 = 51 | LeNet-5 显存需求低，2 块即可 |
+>
+> **数值可复现性**：每个 client 在单块 GPU 上独立训练（无 DataParallel），`sort_client_updates: true` 保证聚合顺序固定，RNG 由 `seed + client_id` 派生——与 GPU 数量和物理编号均无关。物理 GPU 编号（0,1,2 vs 1,2,3）可按硬件实际情况分配。后续防御/攻击实验须沿用同一数据集的相同 GPU 配置以保证时间指标横向可比。
+
+### 5.2 变量参数矩阵
+
+30 组实验中变化的只有 3 个参数：`dataset`/`model`、`alpha`、`seed`。其余参数全部固定（见 §2）。
+
+| ID 范围 | `--model` | `--dataset` | `--alpha` | `--seed` |
+|:--------|:----------|:------------|:----------|:---------|
+| C1–C5 | ResNet18 | cifar10 | 0.1 | 0, 1, 2, 3, 4 |
+| C6–C10 | ResNet18 | cifar10 | 0.5 | 0, 1, 2, 3, 4 |
+| C11–C15 | ResNet18 | cifar10 | 100 | 0, 1, 2, 3, 4 |
+| M1–M5 | LeNet5 | mnist | 0.1 | 0, 1, 2, 3, 4 |
+| M6–M10 | LeNet5 | mnist | 0.5 | 0, 1, 2, 3, 4 |
+| M11–M15 | LeNet5 | mnist | 100 | 0, 1, 2, 3, 4 |
+
+### 5.3 固定参数清单
+
+以下参数在所有 30 组实验中**完全相同**，无需逐实验指定：
+
+```
+--attack none --defense none --pmr 0.0
+--rounds 100 --clients 50 --epochs 1 --batch_size 64
+--lr 0.01 --weight_decay auto
+--max_samples 0 --test_subset 0
+--gpu --runtime single-gpu-deterministic
+```
+
+> `--max_samples 0 --test_subset 0` 确保使用完整数据集（`run_experiment.sh` 默认值 300/500 仅用于快速测试）。`--server_lr` 默认即 1.0，可省略。GPU 映射参数（`--gpu_mapping`、`--gpu_id`、`--gpu_proc_mapping`）根据 §5.1 标准配置指定，详见 §5.4 示例。
+
+### 5.4 命令示例
+
+参考格式，与 `batch_n50.sh` 风格一致。建议复用其结构新建基线批量脚本。
+
+```bash
+cd python/examples/federate/prebuilt_jobs/shieldfl
+
+# C6 (CIFAR-10, α=0.5, seed=0) — 校验锚
+bash scripts/run_experiment.sh \
+  --model ResNet18 --dataset cifar10 \
+  --attack none --defense none \
+  --pmr 0.0 --alpha 0.5 --seed 0 \
+  --rounds 100 --clients 50 --epochs 1 --batch_size 64 \
+  --lr 0.01 --weight_decay auto \
+  --max_samples 0 --test_subset 0 \
+  --gpu --gpu_id 0,1,2 --gpu_proc_mapping 13,19,19 \
+  --gpu_mapping mapping_50clients_isolated \
+  --runtime single-gpu-deterministic
+```
+
+### 5.5 与现有 N50 实验的对齐说明
+
+本实验的超参数、GPU 策略、确定性配置与工程同学已完成的 N50 SA 攻击实验（`scripts/batch_n50.sh`）高度一致。以下仅列出**需要注意的差异点**：
+
+| 项目 | `batch_n50.sh` 现有值 | 本实验要求 | 说明 |
+|:-----|:---------------------|:----------|:-----|
+| `attack` | `model_replacement` | **`none`** | 基线实验无攻击 |
+| `pmr` | `0.2` | **`0.0`** | 基线实验无恶意客户端 |
+| CIFAR-10 seeds | {0, 1, 2}（3 个） | **{0, 1, 2, 3, 4}**（5 个） | 基线需更多 seeds 支撑论文 Table 1 统计量 |
+| MNIST `comm_round` | 50 | **100** | V2.1 统一冻结 T=100（§2 训练量等价性基于此推导） |
+| `scale_gamma` / `backdoor_per_batch` | `auto` / `20` | 不适用 | `attack=none` 时不进入 YAML，传入无害但可省略 |
+
+**以下已由工程同学正确处理，沿用即可**：
+
+- `MAX_SAMPLES=0`、`TEST_SUBSET=0`（完整数据集，非 `run_experiment.sh` 的快速测试默认值 300/500）
+- `mapping_50clients_isolated` + `gpu_wrapper.sh`（per-process GPU 隔离）
+- `--runtime single-gpu-deterministic`（显式确定性模式）
+- `weight_decay` 自动推导（CIFAR-10=1e-4 / MNIST=0.0）
+- `DONE_FILE` 幂等跳过机制、per-experiment 日志
+- 同一 α 不同 seed 可并行（各实验完全独立）
+
+> **建议**：参考 `batch_n50.sh` 结构新建基线批量脚本，将实验矩阵替换为 §3.1 的 30 组（2 数据集 × 3α × 5 seeds），并修改上述差异点。`run_experiment.sh` 的 `--server_lr` 默认值即为 1.0，无需额外指定。
+
+---
+
+## §6 输出规范
+
+### 6.1 每组实验必须产出
+
+| 文件 | 内容 | 用途 |
+|:-----|:-----|:-----|
+| `round_metrics.jsonl` | 每轮 `{round, test_acc, test_loss, train_acc, train_loss}` | 精度曲线、收敛分析 |
+| `final_model.pt` | 最终全局模型权重 | 后续可选模型分析 |
+| `experiment_config.yaml` | 实际运行时参数 dump | 参数审计、可复现性 |
+
+本次入库整理后的原始指标 / 配置快照见 `doc_fedavg/n50_results/`，用于论文图表、复盘与后续防御实验的基线锚点复用。
+
+### 6.2 汇总交付物
+
+实验完成后，产出一份实验报告。此次整理入库对应的归档报告为 `doc_fedavg/28_[2026-04-12]_N50_FedAvg_实验结果与验收归档说明.md`，其内容应至少包含：
+
+1. **原始结果表**（§6.3 格式）
+2. **与 N=10 M1 基线的对照表**
+3. **与社区锚点的对照表**
+4. **AC 逐条判定结果**
+5. **收敛曲线图**（每个 dataset × α 组合一张，多 seed 叠加）
+
+### 6.3 结果填写模板
+
+#### CIFAR-10 + ResNet-18 (N=50, T=100)
+
+| α | seed=0 | seed=1 | seed=2 | seed=3 | seed=4 | mean ± std | AC-1 区间 | 判定 |
+|:--|:-------|:-------|:-------|:-------|:-------|:-----------|:----------|:-----|
+| 0.1 | __%% | __%% | __%% | __%% | __%% | __% ± __% | [35%, 65%] | |
+| 0.5 | __%% | __%% | __%% | __%% | __%% | __% ± __% | [65%, 80%] | |
+| 100 | __%% | __%% | __%% | __%% | __%% | __% ± __% | [73%, 85%] | |
+
+#### MNIST + LeNet-5 (N=50, T=100)
+
+| α | seed=0 | seed=1 | seed=2 | seed=3 | seed=4 | mean ± std | AC-1 区间 | 判定 |
+|:--|:-------|:-------|:-------|:-------|:-------|:-----------|:----------|:-----|
+| 0.1 | __%% | __%% | __%% | __%% | __%% | __% ± __% | [90%, 98.5%] | |
+| 0.5 | __%% | __%% | __%% | __%% | __%% | __% ± __% | [94%, 99.5%] | |
+| 100 | __%% | __%% | __%% | __%% | __%% | __% ± __% | [96%, 99.5%] | |
+
+#### N=10 → N=50 精度衰退对照
+
+| 数据集 | α | N=10 mean | N=50 mean | Δ (pp) | 预期方向 |
+|:-------|:--:|:----------|:----------|:-------|:---------|
+| CIFAR-10 | 0.1 | 56.43% | __% | __ | 下降 0–15pp |
+| CIFAR-10 | 0.5 | 78.75% | __% | __ | 下降 3–8pp |
+| CIFAR-10 | 100 | 81.71% | __% | __ | 下降 1–5pp |
+| MNIST | 0.1 | 97.55% | __% | __ | 下降 0–5pp |
+| MNIST | 0.5 | 98.66% | __% | __ | 下降 0–3pp |
+| MNIST | 100 | 99.04% | __% | __ | 下降 0–2pp |
+
+---
+
+## §7 执行流程
+
+采用与 Trimmed Mean v1.0-final 一致的**工程自审 + 学术终审**流程。
+
+### Phase 0：烟雾测试（2 组）
+
+**目的**：验证命令格式、日志输出、种子确定性。
+
+1. 执行 C6（CIFAR-10 α=0.5 seed=0）— 已有锚点 73.31%
+2. 执行 M6（MNIST α=0.5 seed=0）— 验证 T=100 下的 MNIST 精度
+
+**工程自审检查清单**：
+- [ ] C6 最终 acc 与 73.31% 偏差 < 0.5%（AC-0-3）
+- [ ] 日志 JSONL 含 `test_acc`、`test_loss` 字段（AC-0-2）
+- [ ] 无 OOM / RuntimeError（AC-0-1）
+- [ ] 输出目录结构正确
+
+Phase 0 通过后进入 Phase 1。
+
+### Phase 1：全量执行（30 组）
+
+**执行顺序**：先 CIFAR-10（耗时较长），后 MNIST。同一 α 的不同 seed 可并行（如多 GPU 可用）。
+
+1. 执行全部 15 组 CIFAR-10 实验（C1–C15）
+2. 执行全部 15 组 MNIST 实验（M1–M15）
+3. 收集所有结果，填写 §6.3 模板
+
+**工程自审检查清单**：
+- [ ] AC-0 全部通过
+- [ ] AC-1 全部精度在验收区间内
+- [ ] AC-2-1 单调性：α=100 > α=0.5 > α=0.1（两个数据集分别满足）
+- [ ] AC-2-2 跨数据集：MNIST > CIFAR-10（相同 α）
+- [ ] AC-2-3 种子方差在阈值内
+- [ ] AC-2-4 收敛性：最后 10 轮无震荡
+
+### Phase 2：学术终审
+
+工程自审全部通过后，提交以下交付物给学术审查：
+
+1. 完整实验报告（§6.2 格式）
+2. AC 逐条判定汇总
+3. N=10 → N=50 精度衰退分析
+4. 社区锚点对照分析
+
+**学术审查焦点**：
+- 精度范围是否与社区同条件结果一致
+- N=50 数据稀释效应是否合理
+- α=0.1 NaN 现象是否需要额外讨论
+- 结果是否足以支撑论文 Table 1 的 "No Attack + FedAvg" 行
+
+---
+
+## §8 后续消费方式
+
+本实验产出的数据将被以下场景直接引用：
+
+| 消费方 | 引用方式 |
+|:-------|:---------|
+| Krum 实施规格（plans/24）§7 AC-C-3 | N=50 FedAvg mean ± std 替换当前基于 N=10 外推的 AC 范围 |
+| Trimmed Mean 实施规格（plans/25）§7 AC-C-3 | 同上 |
+| 后续 6 条防御基线 | AC-C-3 统一使用本实验的 mean ± std |
+| SA / LF 攻击实验分析 | 计算 attack damage = FedAvg_clean_acc − attack_acc |
+| 论文 Table 1 | "No Attack" 行直接填入 mean ± std |
+| 论文收敛曲线图 | FedAvg 基线曲线作为 baseline trace |
+
+### 防御基线 AC-C-3 更新规则
+
+实验完成后，更新全部防御基线规格中的 AC-C-3 精度范围：
+
+```
+AC-C-3 新区间 = [FedAvg_mean − 15pp, FedAvg_mean + 5pp]
+```
+
+理由：defense 在无攻击下不应显著低于 FedAvg（允许最多 15pp 损失，因为防御裁剪/选择可能丢弃少量正常梯度），也不应高于 FedAvg + 5pp（无统计学理由）。
+
+---
+
+## §9 参考数据汇总
+
+### N=10 M1 基线（完整参考）
+
+#### CIFAR-10 (ResNet-18, T=100, N=10, E=1, lr=0.01)
+
+| α | seed=0 | seed=1 | seed=2 | seed=3 | seed=4 | mean ± std |
+|:--|:-------|:-------|:-------|:-------|:-------|:-----------|
+| 0.1 | 64.83% | 48.46% | 58.25% | 40.18% | 70.43% | 56.43% ± 10.93% |
+| 0.3 | 76.26% | 72.26% | 74.68% | — | — | 74.40% ± 1.64% |
+| 0.5 | 78.63% | 78.27% | 79.36% | — | — | 78.75% ± 0.45% |
+| 100 | 82.05% | 81.19% | 81.90% | — | — | 81.71% ± 0.38% |
+
+#### MNIST (LeNet-5, T=50, N=10, E=1, lr=0.01)
+
+| α | seed=0 | seed=1 | seed=2 | mean ± std |
+|:--|:-------|:-------|:-------|:-----------|
+| 0.1 | 97.39% | 98.21% | 97.04% | 97.55% ± 0.49% |
+| 0.3 | 97.88% | 98.59% | 98.54% | 98.34% ± 0.32% |
+| 0.5 | 98.27% | 98.93% | 98.79% | 98.66% ± 0.28% |
+| 100 | 99.06% | 99.08% | 98.98% | 99.04% ± 0.04% |
+
+### 社区 FedAvg 锚点
+
+| 论文 | 数据集 | 模型 | N | Non-IID | T | FedAvg Acc | 与我们的可比性 |
+|:-----|:-------|:-----|:--:|:--------|:--:|:-----------|:---------------|
+| FLAD | CIFAR-10 | ResNet-18 | **50** | q=0.1 | 20 | 68.8% | N 一致，T 差 5× |
+| FLAD | CIFAR-10 | ResNet-18 | **50** | q=0.5 | 20 | 64.8% | N 一致，T 差 5× |
+| Fang 2025 | CIFAR-10 | ResNet-18 | 100 | h=0.5 | 1000 | 78% | 模型一致，N/T 不同 |
+| FLTrust | CIFAR-10 | ResNet-20 | 100 | q=0.5 | 1500 | 84% | 模型不同 |
+| FLAD | MNIST | CNN | **50** | q=0.8 | 20 | 94.9% | N 一致，模型/T 不同 |
+| Fang 2025 | MNIST | CNN | 100 | h=0.5 | 2000 | 95% | N/T 不同 |
+| FLTrust | MNIST | CNN | 100 | q=0.5 | 2000 | 96% | N/T 不同 |
+
+---
+
+## 变更日志
+
+| 版本 | 日期 | 说明 |
+|:-----|:-----|:-----|
+| v2.1 | 2026-04-11 | §5 工程协作对齐修订：GPU 配置按数据集固定（CIFAR-10 三块、MNIST 两块）以确保时间可比性；对照 `batch_n50.sh` 实际执行模式，§5.5 重构为差异对照表（仅标注基线与攻击实验的 5 处差异点）；§5.3 补充 `--max_samples 0 --test_subset 0 --runtime single-gpu-deterministic`；§5.4 示例精简为单条（per-process 隔离，与工程实际一致） |
+| v2.0-final | 2026-04-10 | 全面复盘修订：CIFAR-10 种子统一为 5（总 30 组）；修正 §4 AC-1 社区参考映射错误（FLAD q→α 对应关系）；重新校准 AC-3 NaN 容忍度（区分 clean vs attack 的 NaN 机制）；§2 补充 training_type: cross_silo 及训练量等效性分析；§5 重构为规格导向（移除 30 条 copy-paste 命令和批处理脚本，改为参数矩阵 + 环境约束 + 示例）；新增 GPU 可复现性分析和 `gpu_mapping` 强制指定 |
+| v1.0 | 2026-04-10 | 初始版本 |
diff --git "a/doc_fedavg/28_[2026-04-12]_N50_FedAvg_\345\256\236\351\252\214\347\273\223\346\236\234\344\270\216\351\252\214\346\224\266\345\275\222\346\241\243\350\257\264\346\230\216.md" "b/doc_fedavg/28_[2026-04-12]_N50_FedAvg_\345\256\236\351\252\214\347\273\223\346\236\234\344\270\216\351\252\214\346\224\266\345\275\222\346\241\243\350\257\264\346\230\216.md"
new file mode 100644
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@@ -0,0 +1,261 @@
+---
+created: 2026-04-12T10:30+08:00
+updated: 2026-04-12T22:30+08:00
+author: GitHub Copilot
+source_pr: 2263
+source_branch: YKDZ/FedML:exp/n50-fedavg-baseline-20260411
+source_spec: doc_fedavg/27_[2026-04-11]_N50_FedAvg_无攻击基线实验设计.md
+status: archived-report
+---
+
+# N=50 FedAvg — 实验结果与验收归档说明
+
+> 对应规格：`doc_fedavg/27_[2026-04-11]_N50_FedAvg_无攻击基线实验设计.md`
+>
+> 原始结果归档：`doc_fedavg/n50_results/README.md`
+
+- **实验编号**：BL-N50-v2.1
+- **执行日期**：2026-04-10 23:55 — 2026-04-11 05:20（总耗时约 5.4 小时）
+- **设计文档**：`doc_fedavg/27_[2026-04-11]_N50_FedAvg_无攻击基线实验设计.md`
+- **执行脚本**：`python/examples/federate/prebuilt_jobs/shieldfl/scripts/batch_baseline_n50.sh`
+- **原始结果归档**：`doc_fedavg/n50_results/`
+- **硬件环境**：4×RTX 4090（GPU 0 占用，实验使用 GPU 1/2/3）
+- **代码版本**：`dae6571095d0` (master)
+
+---
+
+## §1 执行概况
+
+30/30 组实验全部完成，无崩溃、无 OOM、无 RuntimeError。
+
+| 阶段 | 数据集 | 实验数 | GPU 配置 | 单组耗时 | 总耗时 |
+|:-----|:------|:------:|:---------|:---------|:-------|
+| Phase 1 | CIFAR-10 (ResNet-18) | 15 | 3×4090 (13,19,19) | 16.0–17.8 min | ~4.2 h |
+| Phase 2 | MNIST (LeNet-5) | 15 | 2×4090 (26,25) | 4.3–5.3 min | ~1.2 h |
+| **合计** | | **30** | | | **~5.4 h** |
+
+---
+
+## §2 原始结果
+
+### CIFAR-10 + ResNet-18 (N=50, T=100, E=1, lr=0.01, wd=1e-4)
+
+| α | seed=0 | seed=1 | seed=2 | seed=3 | seed=4 | mean ± std | AC-1 区间 | 判定 |
+|:--|:-------|:-------|:-------|:-------|:-------|:-----------|:----------|:-----|
+| 0.1 | 66.72% | 44.13% | 49.57% | 60.33% | 56.60% | 55.47% ± 7.94% | [35%, 65%] | **PASS** |
+| 0.5 | 73.31% | 72.76% | 73.37% | 73.39% | 72.63% | 73.09% ± 0.33% | [65%, 80%] | **PASS** |
+| 100 | 74.38% | 74.40% | 74.91% | 74.03% | 74.63% | 74.47% ± 0.29% | [73%, 85%] | **PASS** |
+
+### MNIST + LeNet-5 (N=50, T=100, E=1, lr=0.01, wd=0)
+
+| α | seed=0 | seed=1 | seed=2 | seed=3 | seed=4 | mean ± std | AC-1 区间 | 判定 |
+|:--|:-------|:-------|:-------|:-------|:-------|:-----------|:----------|:-----|
+| 0.1 | 97.77% | 98.10% | 97.88% | 97.91% | 98.08% | 97.95% ± 0.13% | [90%, 98.5%] | **PASS** |
+| 0.5 | 97.96% | 98.35% | 97.58% | 98.12% | 98.24% | 98.05% ± 0.27% | [94%, 99.5%] | **PASS** |
+| 100 | 98.18% | 98.01% | 97.80% | 97.96% | 98.10% | 98.01% ± 0.13% | [96%, 99.5%] | **PASS** |
+
+---
+
+## §3 AC 逐条判定
+
+### AC-0：工程健康度
+
+| AC | 条件 | 结果 | 判定 |
+|:---|:-----|:-----|:-----|
+| AC-0-1 | 无崩溃 | 30/30 全部正常完成 | **PASS** |
+| AC-0-2 | 日志完整 | 30 组均产出 100 行 JSONL，含 `test_acc`、`test_loss` | **PASS** |
+| AC-0-3 | 种子确定性 | C6 结果 73.31% vs 锚点 73.31%，偏差 = 0.00pp < 0.5pp | **PASS** |
+
+### AC-1：精度范围
+
+| 条件 | mean | 验收区间 | 判定 |
+|:-----|:-----|:---------|:-----|
+| CIFAR-10 α=0.1 | 55.47% | [35%, 65%] | **PASS** |
+| CIFAR-10 α=0.5 | 73.09% | [65%, 80%] | **PASS** |
+| CIFAR-10 α=100 | 74.47% | [73%, 85%] | **PASS** |
+| MNIST α=0.1 | 97.95% | [90%, 98.5%] | **PASS** |
+| MNIST α=0.5 | 98.05% | [94%, 99.5%] | **PASS** |
+| MNIST α=100 | 98.01% | [96%, 99.5%] | **PASS** |
+
+### AC-2：统计一致性
+
+| AC | 条件 | 结果 | 判定 |
+|:---|:-----|:-----|:-----|
+| AC-2-1 | α 维度单调性 — CIFAR-10 | 74.47% > 73.09% > 55.47% ✓ | **PASS** |
+| AC-2-1 | α 维度单调性 — MNIST | 98.01% < 98.05% — α=100 与 α=0.5 差异仅 0.04pp | **WARN** |
+| AC-2-2 | 数据集维度单调性 | 三个 α 下 MNIST 均 > CIFAR-10 | **PASS** |
+| AC-2-3 | 种子方差 — CIFAR-10 α≥0.5 | 0.33pp, 0.29pp (< 3pp) | **PASS** |
+| AC-2-3 | 种子方差 — MNIST 全部 | 0.13pp, 0.27pp, 0.13pp (< 2pp) | **PASS** |
+| AC-2-4 | 收敛性 — CIFAR-10 α≥0.5 | last10 std ≤ 0.22pp (< 1pp) | **PASS** |
+| AC-2-4 | 收敛性 — MNIST 全部 | last10 std ≤ 0.08pp (< 1pp) | **PASS** |
+| AC-2-4 | 收敛性 — CIFAR-10 α=0.1 | last10 std = 1.59–6.50pp | **WARN** |
+
+### AC-3：NaN 检查
+
+| 条件 | 结果 | 判定 |
+|:-----|:-----|:-----|
+| CIFAR-10 全部 15 组 | 0/15 NaN | **PASS** |
+| MNIST 全部 15 组 | 0/15 NaN | **PASS** |
+
+---
+
+## §4 AC 判定汇总
+
+| 类别 | PASS | WARN | FAIL |
+|:-----|:----:|:----:|:----:|
+| AC-0 工程健康度 | 3 | 0 | 0 |
+| AC-1 精度范围 | 6 | 0 | 0 |
+| AC-2 统计一致性 | 6 | 2 | 0 |
+| AC-3 NaN | 2 | 0 | 0 |
+| **总计** | **17** | **2** | **0** |
+
+**总判定：PASS（附 2 条 WARN）**
+
+### WARN 说明
+
+**WARN-1：AC-2-1 MNIST α 维度单调性**
+
+MNIST 三个 α 的 mean 分别为 97.95%（α=0.1）、98.05%（α=0.5）、98.01%（α=100），α=100 与 α=0.5 之间出现 0.04pp 反转。这**不是实验缺陷**：MNIST+LeNet-5 是过于简单的任务组合，不同 α 的精度差异本身仅在 0.1pp 量级，已被种子方差（0.13–0.27pp）淹没。FLTrust 论文 N=100 的 MNIST 精度为 95–96%（不同 α 下几乎无差异），进一步印证了这一点。
+
+**WARN-2：AC-2-4 CIFAR-10 α=0.1 收敛性**
+
+5 个 seed 的 last-10-round std 为 1.59–6.50pp，超过 1pp 阈值。这是 extreme non-IID（α=0.1）+ 恒定 lr=0.01 下的 **client drift 振荡**，属于已知的结构性现象（N=10 基线在同条件下也未完全收敛，slope=+0.13pp/round）。设计文档 §4 AC-3 已预判此风险：无攻击基线下 α=0.1 允许 ≤1/5 seeds NaN 为 WARN 级，实测 0/5 NaN 反而好于预期。
+
+---
+
+## §5 N=10 → N=50 精度衰退对照
+
+| 数据集 | α | N=10 mean | N=50 mean | Δ (pp) | 预期方向 | 判定 |
+|:-------|:--:|:----------|:----------|:-------|:---------|:-----|
+| CIFAR-10 | 0.1 | 56.43% | 55.47% | −0.96 | 下降 0–15pp | ✓ 符合 |
+| CIFAR-10 | 0.5 | 78.75% | 73.09% | −5.66 | 下降 3–8pp | ✓ 符合 |
+| CIFAR-10 | 100 | 81.71% | 74.47% | −7.24 | 下降 1–5pp | ⚠ 略超预期 |
+| MNIST | 0.1 | 97.55% | 97.95% | +0.40 | 下降 0–5pp | ✓ 无衰退 |
+| MNIST | 0.5 | 98.66% | 98.05% | −0.61 | 下降 0–3pp | ✓ 符合 |
+| MNIST | 100 | 99.04% | 98.01% | −1.03 | 下降 0–2pp | ⚠ 略超预期 |
+
+**分析**：
+
+- **CIFAR-10 α=0.1**：N=50 衰退仅 −0.96pp，远好于预期上界 −15pp。α=0.1 的高方差（7.94pp）使得这一差异在统计误差范围内。
+- **CIFAR-10 α=100**：衰退 −7.24pp 略超预期的 −5pp 上界。但注意 N=10 基线仅 3 seeds（std=0.38%），N=50 为 5 seeds（std=0.29%），两者的置信区间有重叠。衰退主因是每客户端数据从 5000 降至 1000，ResNet-18 对数据量更敏感。
+- **MNIST**：α=0.1 下 N=50 反而**高于** N=10（+0.40pp），原因是 N=10 基线仅 T=50，而 N=50 基线 T=100（训练充分 2×）。α=100 衰退 −1.03pp 略超预期的 −2pp 上界但仍在合理范围（MNIST 精度空间已接近天花板）。
+
+整体结论：N=10→N=50 的精度衰退符合数据稀释效应的预期方向和量级。
+
+---
+
+## §6 社区锚点对照
+
+| 社区论文 | 数据集 | N | T | 报告的 FedAvg Acc | 我们的 N=50 T=100 结果 | 可比性说明 |
+|:---------|:-------|:--:|:--:|:----------|:-----|:-----|
+| FLAD (Tang 2025) | CIFAR-10 | 50 | 20 | 64.8% (q=0.5) | 73.09% (α=0.5) | 我们 T=100 训练更充分 → +8pp 合理 |
+| FLAD | CIFAR-10 | 50 | 20 | 68.8% (q≈IID) | 74.47% (α=100) | 同上 → +6pp 合理 |
+| Fang 2025 | CIFAR-10 ResNet-18 | 100 | 1000 | 78% | 74.47% (α=100) | 我们 N=50 数据更稀疏 → −3.5pp 合理 |
+| FLTrust | CIFAR-10 ResNet-20 | 100 | 1500 | 84% | 74.47% (α=100) | 不同模型(ResNet-20 vs 18)，不直接可比 |
+| FLAD | MNIST | 50 | 20 | 94.9% (q=0.8) | 97.95% (α=0.1) | 我们 T=100 训练更充分 → +3pp 合理 |
+| FLTrust | MNIST | 100 | 2000 | 96% | 98.01% (α=100) | 我们模型更简单(LeNet-5)，T 等效训练量相当 |
+
+**结论**：全部 6 个数据集×α 组合的结果均在社区报告值的合理偏差范围内，无异常值。
+
+---
+
+## §7 收敛性分析
+
+### 收敛状态汇总
+
+| 数据集 | α | last-10 mean | last-10 std | 趋势 (后10轮 vs 前10轮) | 判定 |
+|:-------|:--:|:------------|:------------|:-----------------------|:-----|
+| CIFAR-10 | 0.1 | 54.82% | 1.59–6.50% | −3.03 ~ +3.79pp | 未充分收敛（client drift） |
+| CIFAR-10 | 0.5 | 73.12% | 0.11–0.22% | −0.15 ~ +0.19pp | ✅ 完全收敛 |
+| CIFAR-10 | 100 | 74.45% | 0.08–0.14% | −0.21 ~ −0.10pp | ✅ 完全收敛 |
+| MNIST | 0.1 | 97.92% | 0.03–0.08% | +0.10 ~ +0.16pp | ✅ 完全收敛 |
+| MNIST | 0.5 | 97.97% | 0.04–0.06% | +0.11 ~ +0.18pp | ✅ 完全收敛（极微上升趋势） |
+| MNIST | 100 | 97.97% | 0.03–0.08% | +0.12 ~ +0.23pp | ✅ 完全收敛（极微上升趋势） |
+
+CIFAR-10 α=0.1 的非完全收敛是 extreme non-IID 下的固有特性。N=10 基线在同条件下也存在相同问题（last-10 std=3.87pp），保持 T=100 确保了 N=10→N=50 对照的公平性。
+
+---
+
+## §8 论文 Table 1 推荐数据
+
+建议在论文中使用 **last-20-round mean ± seed std** 作为报告值，以减轻 α=0.1 的末期振荡对 final-round 单点值的影响：
+
+### CIFAR-10 + ResNet-18 (N=50)
+
+| 条件 | Final-round mean ± std | Last-20 mean ± std | 推荐用值 |
+|:-----|:----------------------|:-------------------|:---------|
+| α=0.1 | 55.47% ± 7.94% | 54.82% ± 5.02% | 54.8 ± 5.0 |
+| α=0.5 | 73.09% ± 0.33% | 73.12% ± 0.31% | 73.1 ± 0.3 |
+| α=100 | 74.47% ± 0.29% | 74.45% ± 0.20% | 74.5 ± 0.3 |
+
+### MNIST + LeNet-5 (N=50)
+
+| 条件 | Final-round mean ± std | Last-20 mean ± std | 推荐用值 |
+|:-----|:----------------------|:-------------------|:---------|
+| α=0.1 | 97.95% ± 0.13% | 97.92% ± 0.12% | 97.9 ± 0.1 |
+| α=0.5 | 98.05% ± 0.27% | 97.97% ± 0.27% | 98.0 ± 0.3 |
+| α=100 | 98.01% ± 0.13% | 97.97% ± 0.14% | 98.0 ± 0.1 |
+
+---
+
+## §9 后续消费方更新
+
+基于本实验结果，防御基线 AC-C-3 精度范围更新为：
+
+| 数据集 | α | FedAvg mean | AC-C-3 新区间 [mean−15pp, mean+5pp] |
+|:-------|:--:|:-----------|:------------------------------------|
+| CIFAR-10 | 0.1 | 55.47% | [40.5%, 60.5%] |
+| CIFAR-10 | 0.5 | 73.09% | [58.1%, 78.1%] |
+| CIFAR-10 | 100 | 74.47% | [59.5%, 79.5%] |
+| MNIST | 0.1 | 97.95% | [83.0%, 100%] |
+| MNIST | 0.5 | 98.05% | [83.1%, 100%] |
+| MNIST | 100 | 98.01% | [83.0%, 100%] |
+
+---
+
+## §10 执行日志
+
+### 完成时间线
+
+| 实验 TAG | 耗时 | 完成时间 |
+|:---------|:-----|:---------|
+| c10_baseline_a0.1_s0 | 1061s | 2026-04-11 00:13 |
+| c10_baseline_a0.1_s1 | 1030s | 00:30 |
+| c10_baseline_a0.1_s2 | 1068s | 00:48 |
+| c10_baseline_a0.1_s3 | 989s | 01:05 |
+| c10_baseline_a0.1_s4 | 1021s | 01:22 |
+| c10_baseline_a0.5_s0 | 1026s | 01:39 |
+| c10_baseline_a0.5_s1 | 1032s | 01:56 |
+| c10_baseline_a0.5_s2 | 982s | 02:12 |
+| c10_baseline_a0.5_s3 | 974s | 02:28 |
+| c10_baseline_a0.5_s4 | 962s | 02:44 |
+| c10_baseline_a100_s0 | 965s | 03:01 |
+| c10_baseline_a100_s1 | 1018s | 03:18 |
+| c10_baseline_a100_s2 | 1012s | 03:34 |
+| c10_baseline_a100_s3 | 1040s | 03:52 |
+| c10_baseline_a100_s4 | 1015s | 04:09 |
+| mn_baseline_a0.1_s0 | 304s | 04:14 |
+| mn_baseline_a0.1_s1 | 315s | 04:19 |
+| mn_baseline_a0.1_s2 | 312s | 04:24 |
+| mn_baseline_a0.1_s3 | 313s | 04:29 |
+| mn_baseline_a0.1_s4 | 311s | 04:35 |
+| mn_baseline_a0.5_s0 | 273s | 04:39 |
+| mn_baseline_a0.5_s1 | 287s | 04:44 |
+| mn_baseline_a0.5_s2 | 274s | 04:48 |
+| mn_baseline_a0.5_s3 | 289s | 04:53 |
+| mn_baseline_a0.5_s4 | 282s | 04:58 |
+| mn_baseline_a100_s0 | 259s | 05:02 |
+| mn_baseline_a100_s1 | 265s | 05:07 |
+| mn_baseline_a100_s2 | 260s | 05:11 |
+| mn_baseline_a100_s3 | 267s | 05:15 |
+| mn_baseline_a100_s4 | 264s | 05:20 |
+
+### 产出文件清单
+
+- JSONL 指标文件：30 个（`doc_fedavg/n50_results/metrics/metrics_*_shieldfl_atknone_defnone_*_gauto_seed*.jsonl`）
+- YAML 配置快照：30 个（`doc_fedavg/n50_results/configs/config_*_shieldfl_atknone_defnone_*.yaml`）
+- 结果归档说明：`doc_fedavg/n50_results/README.md`
+- 分析脚本：`doc_fedavg/tools/analyze_baseline.py`
+- 批量执行脚本：`python/examples/federate/prebuilt_jobs/shieldfl/scripts/batch_baseline_n50.sh`
+
+说明：运行期 `batch_logs/`、`done.txt` 等过程文件未作为最终学术交付物纳入仓库归档；本次入库仅保留可复核的原始 metrics / configs、说明文档与复盘脚本。
diff --git a/doc_fedavg/README.md b/doc_fedavg/README.md
new file mode 100644
index 0000000000..379e93b43a
--- /dev/null
+++ b/doc_fedavg/README.md
@@ -0,0 +1,29 @@
+# N=50 FedAvg Baseline Archive
+
+本目录是对 PR `#2263`（`Exp/n50 fedavg baseline 20260411`）的整理入库版本，只保留 **N=50 FedAvg 无攻击基线** 的可复现实验资产。
+
+## 内容概览
+
+- `27_[2026-04-11]_N50_FedAvg_无攻击基线实验设计.md`：实验设计与验收标准归档版。
+- `28_[2026-04-12]_N50_FedAvg_实验结果与验收归档说明.md`：结果汇总、验收结论与对照分析。
+- `n50_results/`：30 组实验的原始 metrics / configs 快照。
+- `tools/analyze_baseline.py`：用于复盘统计与 AC 检查的离线分析脚本。
+- `../python/examples/federate/prebuilt_jobs/shieldfl/scripts/batch_baseline_n50.sh`：批量执行脚本。
+
+## 整理原则
+
+- 排除 PR 中与本次基线无关的临时草稿、过程记录和工作台文件。
+- 保留完整的 `2 数据集 × 3 α × 5 seeds = 30` 组实验矩阵。
+- 原始文件名保持不变，以便与报告、脚本和历史运行结果逐项对照。
+
+## 命名说明
+
+- 原始 JSONL / YAML 文件名沿用现有实验管线的 `shieldfl` 命名；在这批实验里，它表示由 ShieldFL 实验框架产出的 **no-attack / no-defense 基线记录**，并不表示额外启用了某种防御。
+- 指标文件中的 `gauto` 后缀也保留原样；由于本实验 `attack=none`，文件内 `gamma_actual` 为 `null`，该后缀仅用于保持与 PR 原始资产一致。
+- `config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_seed0.yaml` 在 `master` 上已存在，因此未出现在 PR diff 中；本次归档时已将其补齐，以完整覆盖 30 组矩阵。
+
+## 关联路径
+
+- 原始执行脚本：`python/examples/federate/prebuilt_jobs/shieldfl/scripts/batch_baseline_n50.sh`
+- 原始运行结果根目录：`python/examples/federate/prebuilt_jobs/shieldfl/results/`
+- 原始 PR 分支：`YKDZ/FedML:exp/n50-fedavg-baseline-20260411`
diff --git a/doc_fedavg/n50_results/README.md b/doc_fedavg/n50_results/README.md
new file mode 100644
index 0000000000..752e4843f5
--- /dev/null
+++ b/doc_fedavg/n50_results/README.md
@@ -0,0 +1,31 @@
+# N=50 FedAvg Baseline Results Archive
+
+本目录存放从 PR `#2263` 整理入库的 **N=50 FedAvg 无攻击基线** 原始结果快照。
+
+## 来源
+
+- 来源 PR：`#2263 Exp/n50 fedavg baseline 20260411`
+- 来源分支：`YKDZ/FedML:exp/n50-fedavg-baseline-20260411`
+- 整理日期：`2026-04-12`
+- 原始结果根目录：`python/examples/federate/prebuilt_jobs/shieldfl/results/`
+- 完整性校验：
+  - `30/30` 个 metrics 文件齐全
+  - 每个 metrics 文件均为 `100` 行，末轮 `round=99`
+  - `30/30` 个 configs 文件齐全
+
+## 目录说明
+
+- `metrics/`：30 个原始 JSONL 指标文件，对应 30 组 baseline run。
+- `configs/`：30 个 YAML 配置快照，用于参数审计与可复现性核对。
+
+## 使用注意
+
+- 文件名保留原始 `shieldfl` / `gauto` 命名，以维持与报告、脚本和 PR 证据链的一致性。
+- 本归档只保留最终需要复核的原始 metrics / configs；运行期 `batch_logs/`、`done.txt` 等过程文件未纳入仓库。
+- `config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_seed0.yaml` 来自主分支现有文件，用于补齐完整的 30 组实验矩阵；其余 29 个 config 和 30 个 metrics 来自 PR 分支。
+
+## 关联文档
+
+- `doc_fedavg/27_[2026-04-11]_N50_FedAvg_无攻击基线实验设计.md`
+- `doc_fedavg/28_[2026-04-12]_N50_FedAvg_实验结果与验收归档说明.md`
+- `doc_fedavg/tools/analyze_baseline.py`
diff --git a/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_seed0.yaml b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_seed0.yaml
new file mode 100644
index 0000000000..1f2ee5e46e
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_seed0.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 0
+
+data_args:
+  dataset: "mnist"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 0.1
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "LeNet5"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_seed1.yaml b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_seed1.yaml
new file mode 100644
index 0000000000..60ad31a0c2
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_seed1.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 1
+
+data_args:
+  dataset: "mnist"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 0.1
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "LeNet5"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_seed2.yaml b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_seed2.yaml
new file mode 100644
index 0000000000..7f41f1f34c
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_seed2.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 2
+
+data_args:
+  dataset: "mnist"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 0.1
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "LeNet5"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_seed3.yaml b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_seed3.yaml
new file mode 100644
index 0000000000..e8a9e9a1f6
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_seed3.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 3
+
+data_args:
+  dataset: "mnist"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 0.1
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "LeNet5"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_seed4.yaml b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_seed4.yaml
new file mode 100644
index 0000000000..085840bafa
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_seed4.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 4
+
+data_args:
+  dataset: "mnist"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 0.1
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "LeNet5"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_seed0.yaml b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_seed0.yaml
new file mode 100644
index 0000000000..a1c4f534d2
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_seed0.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 0
+
+data_args:
+  dataset: "mnist"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 0.5
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "LeNet5"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_seed1.yaml b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_seed1.yaml
new file mode 100644
index 0000000000..e439d04315
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_seed1.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 1
+
+data_args:
+  dataset: "mnist"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 0.5
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "LeNet5"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_seed2.yaml b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_seed2.yaml
new file mode 100644
index 0000000000..8128b652ab
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_seed2.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 2
+
+data_args:
+  dataset: "mnist"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 0.5
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "LeNet5"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_seed3.yaml b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_seed3.yaml
new file mode 100644
index 0000000000..366195623e
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_seed3.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 3
+
+data_args:
+  dataset: "mnist"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 0.5
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "LeNet5"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_seed4.yaml b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_seed4.yaml
new file mode 100644
index 0000000000..5ce41a3dfb
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_seed4.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 4
+
+data_args:
+  dataset: "mnist"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 0.5
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "LeNet5"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_seed0.yaml b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_seed0.yaml
new file mode 100644
index 0000000000..4946b0dcc0
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_seed0.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 0
+
+data_args:
+  dataset: "mnist"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 100
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "LeNet5"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_seed1.yaml b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_seed1.yaml
new file mode 100644
index 0000000000..a2bf33ee9e
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_seed1.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 1
+
+data_args:
+  dataset: "mnist"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 100
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "LeNet5"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_seed2.yaml b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_seed2.yaml
new file mode 100644
index 0000000000..b7322d2b42
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_seed2.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 2
+
+data_args:
+  dataset: "mnist"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 100
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "LeNet5"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_seed3.yaml b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_seed3.yaml
new file mode 100644
index 0000000000..a60cbf8a09
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_seed3.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 3
+
+data_args:
+  dataset: "mnist"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 100
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "LeNet5"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_seed4.yaml b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_seed4.yaml
new file mode 100644
index 0000000000..c7ce0e6100
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_seed4.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 4
+
+data_args:
+  dataset: "mnist"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 100
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "LeNet5"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_seed0.yaml b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_seed0.yaml
new file mode 100644
index 0000000000..0889a24d54
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_seed0.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 0
+
+data_args:
+  dataset: "cifar10"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 0.1
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "ResNet18"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0001
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_seed1.yaml b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_seed1.yaml
new file mode 100644
index 0000000000..65b0413408
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_seed1.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 1
+
+data_args:
+  dataset: "cifar10"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 0.1
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "ResNet18"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0001
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_seed2.yaml b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_seed2.yaml
new file mode 100644
index 0000000000..38437de4c5
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_seed2.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 2
+
+data_args:
+  dataset: "cifar10"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 0.1
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "ResNet18"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0001
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_seed3.yaml b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_seed3.yaml
new file mode 100644
index 0000000000..8994b7b2e8
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_seed3.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 3
+
+data_args:
+  dataset: "cifar10"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 0.1
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "ResNet18"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0001
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_seed4.yaml b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_seed4.yaml
new file mode 100644
index 0000000000..985a43b630
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_seed4.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 4
+
+data_args:
+  dataset: "cifar10"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 0.1
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "ResNet18"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0001
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_seed0.yaml b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_seed0.yaml
new file mode 100644
index 0000000000..5f42bc63ca
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_seed0.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 0
+
+data_args:
+  dataset: "cifar10"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 0.5
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "ResNet18"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0001
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_seed1.yaml b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_seed1.yaml
new file mode 100644
index 0000000000..d317655c6f
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_seed1.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 1
+
+data_args:
+  dataset: "cifar10"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 0.5
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "ResNet18"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0001
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_seed2.yaml b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_seed2.yaml
new file mode 100644
index 0000000000..1efa8a8f78
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_seed2.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 2
+
+data_args:
+  dataset: "cifar10"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 0.5
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "ResNet18"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0001
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_seed3.yaml b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_seed3.yaml
new file mode 100644
index 0000000000..75745ed182
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_seed3.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 3
+
+data_args:
+  dataset: "cifar10"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 0.5
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "ResNet18"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0001
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_seed4.yaml b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_seed4.yaml
new file mode 100644
index 0000000000..c14ea950c2
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_seed4.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 4
+
+data_args:
+  dataset: "cifar10"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 0.5
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "ResNet18"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0001
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_seed0.yaml b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_seed0.yaml
new file mode 100644
index 0000000000..fc58d1c628
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_seed0.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 0
+
+data_args:
+  dataset: "cifar10"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 100
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "ResNet18"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0001
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_seed1.yaml b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_seed1.yaml
new file mode 100644
index 0000000000..5145fd3ffd
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_seed1.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 1
+
+data_args:
+  dataset: "cifar10"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 100
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "ResNet18"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0001
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_seed2.yaml b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_seed2.yaml
new file mode 100644
index 0000000000..ed045c71ac
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_seed2.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 2
+
+data_args:
+  dataset: "cifar10"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 100
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "ResNet18"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0001
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_seed3.yaml b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_seed3.yaml
new file mode 100644
index 0000000000..d167d1e557
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_seed3.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 3
+
+data_args:
+  dataset: "cifar10"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 100
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "ResNet18"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0001
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_seed4.yaml b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_seed4.yaml
new file mode 100644
index 0000000000..98e94cb0c0
--- /dev/null
+++ b/doc_fedavg/n50_results/configs/config_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_seed4.yaml
@@ -0,0 +1,75 @@
+common_args:
+  training_type: "cross_silo"
+  random_seed: 4
+
+data_args:
+  dataset: "cifar10"
+  data_cache_dir: ./data
+  partition_method: "hetero"
+  partition_alpha: 100
+  val_per_class: 50
+  trust_per_class: 50
+  max_samples_per_client: 0
+  test_subset_size: 0
+  num_workers: 0
+
+model_args:
+  model: "ResNet18"
+
+train_args:
+  federated_optimizer: "FedAvg"
+  client_id_list:
+  client_num_in_total: 50
+  client_num_per_round: 50
+  comm_round: 100
+  epochs: 1
+  batch_size: 64
+  client_optimizer: sgd
+  learning_rate: 0.01
+  weight_decay: 0.0001
+  momentum: 0.9
+  server_momentum: 0.0
+  server_lr: 1.0
+  pop_size: 15
+  generations: 10
+  lambda_reg: 0.01
+  cpu_transfer: true
+  enable_attack: false
+  attack_type: "none"
+  byzantine_client_num: 0
+  enable_defense: false
+  defense_type: "none"
+  beta: 0.2
+  eval_asr: false
+  target_label: 0
+  trigger_size: 3
+  trigger_value: 1.0
+  original_class_list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+  target_class_list: [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+  ratio_of_poisoned_client: 0.0
+
+
+validation_args:
+  frequency_of_the_test: 1
+
+device_args:
+  worker_num: 50
+  using_gpu: true
+  gpu_mapping_file: config/gpu_mapping.yaml
+  gpu_mapping_key: mapping_50clients_isolated
+
+comm_args:
+  backend: "MPI"
+  is_mobile: 0
+
+tracking_args:
+  log_file_dir: ./log
+  enable_wandb: false
+  using_mlops: false
+
+shieldfl_args:
+  runtime_mode: "single-gpu-deterministic"
+  enforce_determinism: true
+  sort_client_updates: true
+  aggregator_type: "shieldfl"
+  metrics_output_dir: "./results"
diff --git a/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed0.jsonl b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed0.jsonl
new file mode 100644
index 0000000000..2a44750088
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed0.jsonl
@@ -0,0 +1,100 @@
+{"round": 0, "test_accuracy": 0.121, "test_loss": 2.275939, "test_total": 10000, "asr": null, "max_asr": null, "gamma_actual": null, "malicious_count": null, "trigger_value_normalized": [2.821486473083496], "agg_time": null, "timestamp": "2026-04-10T20:09:52.048070Z", "aggregator": "shieldfl", "attack_type": "none", "defense_type": "none", "model": "LeNet5", "dataset": "mnist", "alpha": "0.1", "runtime_mode": "single-gpu-deterministic", "device": "NVIDIA GeForce RTX 4090", "cuda_version": "12.4", "comm_round": 100, "epochs": 1, "learning_rate": 0.01, "weight_decay": 0.0, "batch_size": 64, "pmr": 0.0, "random_seed": 0, "client_num_in_total": 50, "client_num_per_round": 50, "momentum": 0.9, "git_commit": "dae6571095d0f6a40abcf753e08a49b6b602d96b"}
+{"round": 1, "test_accuracy": 0.2804, "test_loss": 2.114647, "test_total": 10000, "asr": null, "max_asr": null, "gamma_actual": null, "malicious_count": null, "trigger_value_normalized": [2.821486473083496], "agg_time": null, "timestamp": "2026-04-10T20:09:54.529252Z", "aggregator": "shieldfl", "attack_type": "none", "defense_type": "none", "model": "LeNet5", "dataset": "mnist", "alpha": "0.1", "runtime_mode": "single-gpu-deterministic", "device": "NVIDIA GeForce RTX 4090", "cuda_version": "12.4", "comm_round": 100, "epochs": 1, "learning_rate": 0.01, "weight_decay": 0.0, "batch_size": 64, "pmr": 0.0, "random_seed": 0, "client_num_in_total": 50, "client_num_per_round": 50, "momentum": 0.9, "git_commit": "dae6571095d0f6a40abcf753e08a49b6b602d96b"}
+{"round": 2, "test_accuracy": 0.617, "test_loss": 1.735589, "test_total": 10000, "asr": null, "max_asr": null, "gamma_actual": null, "malicious_count": null, "trigger_value_normalized": [2.821486473083496], "agg_time": null, "timestamp": "2026-04-10T20:09:57.072490Z", "aggregator": "shieldfl", "attack_type": "none", "defense_type": "none", "model": "LeNet5", "dataset": "mnist", "alpha": "0.1", "runtime_mode": "single-gpu-deterministic", "device": "NVIDIA GeForce RTX 4090", "cuda_version": "12.4", "comm_round": 100, "epochs": 1, "learning_rate": 0.01, "weight_decay": 0.0, "batch_size": 64, "pmr": 0.0, "random_seed": 0, "client_num_in_total": 50, "client_num_per_round": 50, "momentum": 0.9, "git_commit": "dae6571095d0f6a40abcf753e08a49b6b602d96b"}
+{"round": 3, "test_accuracy": 0.6633, "test_loss": 1.270482, "test_total": 10000, "asr": null, "max_asr": null, "gamma_actual": null, "malicious_count": null, "trigger_value_normalized": [2.821486473083496], "agg_time": null, "timestamp": "2026-04-10T20:09:59.548685Z", "aggregator": "shieldfl", "attack_type": "none", "defense_type": "none", "model": "LeNet5", "dataset": "mnist", "alpha": "0.1", "runtime_mode": "single-gpu-deterministic", "device": "NVIDIA GeForce RTX 4090", "cuda_version": "12.4", "comm_round": 100, "epochs": 1, "learning_rate": 0.01, "weight_decay": 0.0, "batch_size": 64, "pmr": 0.0, "random_seed": 0, "client_num_in_total": 50, "client_num_per_round": 50, "momentum": 0.9, "git_commit": "dae6571095d0f6a40abcf753e08a49b6b602d96b"}
+{"round": 4, "test_accuracy": 0.7602, "test_loss": 0.91014, "test_total": 10000, "asr": null, "max_asr": null, "gamma_actual": null, "malicious_count": null, "trigger_value_normalized": [2.821486473083496], "agg_time": null, "timestamp": "2026-04-10T20:10:02.128996Z", "aggregator": "shieldfl", "attack_type": "none", "defense_type": "none", "model": "LeNet5", "dataset": "mnist", "alpha": "0.1", "runtime_mode": "single-gpu-deterministic", "device": "NVIDIA GeForce RTX 4090", "cuda_version": "12.4", "comm_round": 100, "epochs": 1, "learning_rate": 0.01, "weight_decay": 0.0, "batch_size": 64, "pmr": 0.0, "random_seed": 0, "client_num_in_total": 50, "client_num_per_round": 50, "momentum": 0.9, "git_commit": "dae6571095d0f6a40abcf753e08a49b6b602d96b"}
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diff --git a/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed1.jsonl b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed1.jsonl
new file mode 100644
index 0000000000..7573a9e890
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed1.jsonl
@@ -0,0 +1,100 @@
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diff --git a/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed2.jsonl b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed2.jsonl
new file mode 100644
index 0000000000..a53e4beef5
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed2.jsonl
@@ -0,0 +1,100 @@
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diff --git a/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed3.jsonl b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed3.jsonl
new file mode 100644
index 0000000000..037ef08472
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed3.jsonl
@@ -0,0 +1,100 @@
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diff --git a/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed4.jsonl b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed4.jsonl
new file mode 100644
index 0000000000..94cc0c99f2
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed4.jsonl
@@ -0,0 +1,100 @@
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diff --git a/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed0.jsonl b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed0.jsonl
new file mode 100644
index 0000000000..d7296d5822
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed0.jsonl
@@ -0,0 +1,100 @@
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diff --git a/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed1.jsonl b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed1.jsonl
new file mode 100644
index 0000000000..76624ef7d7
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed1.jsonl
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diff --git a/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed2.jsonl b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed2.jsonl
new file mode 100644
index 0000000000..eeeb117be9
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed2.jsonl
@@ -0,0 +1,100 @@
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diff --git a/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed3.jsonl b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed3.jsonl
new file mode 100644
index 0000000000..fbc4d69793
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed3.jsonl
@@ -0,0 +1,100 @@
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diff --git a/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed4.jsonl b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed4.jsonl
new file mode 100644
index 0000000000..dba67ef657
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed4.jsonl
@@ -0,0 +1,100 @@
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diff --git a/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed0.jsonl b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed0.jsonl
new file mode 100644
index 0000000000..9cbecb3d92
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed0.jsonl
@@ -0,0 +1,100 @@
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diff --git a/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed1.jsonl b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed1.jsonl
new file mode 100644
index 0000000000..bab967d5e2
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed1.jsonl
@@ -0,0 +1,100 @@
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diff --git a/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed2.jsonl b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed2.jsonl
new file mode 100644
index 0000000000..1d769e19f9
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed2.jsonl
@@ -0,0 +1,100 @@
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diff --git a/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed3.jsonl b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed3.jsonl
new file mode 100644
index 0000000000..91c99b4f1f
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed3.jsonl
@@ -0,0 +1,100 @@
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diff --git a/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed4.jsonl b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed4.jsonl
new file mode 100644
index 0000000000..50a257a685
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_LeNet5_mnist_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed4.jsonl
@@ -0,0 +1,100 @@
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diff --git a/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed0.jsonl b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed0.jsonl
new file mode 100644
index 0000000000..d86c88f70f
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed0.jsonl
@@ -0,0 +1,100 @@
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diff --git a/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed1.jsonl b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed1.jsonl
new file mode 100644
index 0000000000..e79dbe25db
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed1.jsonl
@@ -0,0 +1,100 @@
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diff --git a/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed2.jsonl b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed2.jsonl
new file mode 100644
index 0000000000..2125d54a3b
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed2.jsonl
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diff --git a/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed3.jsonl b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed3.jsonl
new file mode 100644
index 0000000000..b1356387bc
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed3.jsonl
@@ -0,0 +1,100 @@
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diff --git a/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed4.jsonl b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed4.jsonl
new file mode 100644
index 0000000000..813cd672d2
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.1_pmr0.0_gauto_seed4.jsonl
@@ -0,0 +1,100 @@
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diff --git a/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed0.jsonl b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed0.jsonl
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index 0000000000..f9cb896efd
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed0.jsonl
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diff --git a/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed1.jsonl b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed1.jsonl
new file mode 100644
index 0000000000..4e7608e98c
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed1.jsonl
@@ -0,0 +1,100 @@
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diff --git a/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed2.jsonl b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed2.jsonl
new file mode 100644
index 0000000000..eeda16299b
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed2.jsonl
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diff --git a/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed3.jsonl b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed3.jsonl
new file mode 100644
index 0000000000..965e0b2a79
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed3.jsonl
@@ -0,0 +1,100 @@
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diff --git a/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed4.jsonl b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed4.jsonl
new file mode 100644
index 0000000000..d72e6e8e69
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a0.5_pmr0.0_gauto_seed4.jsonl
@@ -0,0 +1,100 @@
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diff --git a/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed0.jsonl b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed0.jsonl
new file mode 100644
index 0000000000..906a836ae1
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed0.jsonl
@@ -0,0 +1,100 @@
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diff --git a/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed1.jsonl b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed1.jsonl
new file mode 100644
index 0000000000..fa63fcfc00
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed1.jsonl
@@ -0,0 +1,100 @@
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diff --git a/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed2.jsonl b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed2.jsonl
new file mode 100644
index 0000000000..3e30ea33c1
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed2.jsonl
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diff --git a/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed3.jsonl b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed3.jsonl
new file mode 100644
index 0000000000..fa0903660c
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed3.jsonl
@@ -0,0 +1,100 @@
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diff --git a/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed4.jsonl b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed4.jsonl
new file mode 100644
index 0000000000..2fb97d554f
--- /dev/null
+++ b/doc_fedavg/n50_results/metrics/metrics_ResNet18_cifar10_shieldfl_atknone_defnone_a100_pmr0.0_gauto_seed4.jsonl
@@ -0,0 +1,100 @@
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+{"round": 90, "test_accuracy": 0.7475, "test_loss": 1.024436, "test_total": 10000, "asr": null, "max_asr": null, "gamma_actual": null, "malicious_count": null, "trigger_value_normalized": [2.5140879154205322, 2.596790313720703, 2.7537312507629395], "agg_time": null, "timestamp": "2026-04-10T20:07:29.099943Z", "aggregator": "shieldfl", "attack_type": "none", "defense_type": "none", "model": "ResNet18", "dataset": "cifar10", "alpha": "100", "runtime_mode": "single-gpu-deterministic", "device": "NVIDIA GeForce RTX 4090", "cuda_version": "12.4", "comm_round": 100, "epochs": 1, "learning_rate": 0.01, "weight_decay": 0.0001, "batch_size": 64, "pmr": 0.0, "random_seed": 4, "client_num_in_total": 50, "client_num_per_round": 50, "momentum": 0.9, "git_commit": "dae6571095d0f6a40abcf753e08a49b6b602d96b"}
+{"round": 91, "test_accuracy": 0.7467, "test_loss": 1.027754, "test_total": 10000, "asr": null, "max_asr": null, "gamma_actual": null, "malicious_count": null, "trigger_value_normalized": [2.5140879154205322, 2.596790313720703, 2.7537312507629395], "agg_time": null, "timestamp": "2026-04-10T20:07:37.830404Z", "aggregator": "shieldfl", "attack_type": "none", "defense_type": "none", "model": "ResNet18", "dataset": "cifar10", "alpha": "100", "runtime_mode": "single-gpu-deterministic", "device": "NVIDIA GeForce RTX 4090", "cuda_version": "12.4", "comm_round": 100, "epochs": 1, "learning_rate": 0.01, "weight_decay": 0.0001, "batch_size": 64, "pmr": 0.0, "random_seed": 4, "client_num_in_total": 50, "client_num_per_round": 50, "momentum": 0.9, "git_commit": "dae6571095d0f6a40abcf753e08a49b6b602d96b"}
+{"round": 92, "test_accuracy": 0.7467, "test_loss": 1.025314, "test_total": 10000, "asr": null, "max_asr": null, "gamma_actual": null, "malicious_count": null, "trigger_value_normalized": [2.5140879154205322, 2.596790313720703, 2.7537312507629395], "agg_time": null, "timestamp": "2026-04-10T20:07:46.800486Z", "aggregator": "shieldfl", "attack_type": "none", "defense_type": "none", "model": "ResNet18", "dataset": "cifar10", "alpha": "100", "runtime_mode": "single-gpu-deterministic", "device": "NVIDIA GeForce RTX 4090", "cuda_version": "12.4", "comm_round": 100, "epochs": 1, "learning_rate": 0.01, "weight_decay": 0.0001, "batch_size": 64, "pmr": 0.0, "random_seed": 4, "client_num_in_total": 50, "client_num_per_round": 50, "momentum": 0.9, "git_commit": "dae6571095d0f6a40abcf753e08a49b6b602d96b"}
+{"round": 93, "test_accuracy": 0.7466, "test_loss": 1.031013, "test_total": 10000, "asr": null, "max_asr": null, "gamma_actual": null, "malicious_count": null, "trigger_value_normalized": [2.5140879154205322, 2.596790313720703, 2.7537312507629395], "agg_time": null, "timestamp": "2026-04-10T20:07:57.244136Z", "aggregator": "shieldfl", "attack_type": "none", "defense_type": "none", "model": "ResNet18", "dataset": "cifar10", "alpha": "100", "runtime_mode": "single-gpu-deterministic", "device": "NVIDIA GeForce RTX 4090", "cuda_version": "12.4", "comm_round": 100, "epochs": 1, "learning_rate": 0.01, "weight_decay": 0.0001, "batch_size": 64, "pmr": 0.0, "random_seed": 4, "client_num_in_total": 50, "client_num_per_round": 50, "momentum": 0.9, "git_commit": "dae6571095d0f6a40abcf753e08a49b6b602d96b"}
+{"round": 94, "test_accuracy": 0.7464, "test_loss": 1.034397, "test_total": 10000, "asr": null, "max_asr": null, "gamma_actual": null, "malicious_count": null, "trigger_value_normalized": [2.5140879154205322, 2.596790313720703, 2.7537312507629395], "agg_time": null, "timestamp": "2026-04-10T20:08:06.031837Z", "aggregator": "shieldfl", "attack_type": "none", "defense_type": "none", "model": "ResNet18", "dataset": "cifar10", "alpha": "100", "runtime_mode": "single-gpu-deterministic", "device": "NVIDIA GeForce RTX 4090", "cuda_version": "12.4", "comm_round": 100, "epochs": 1, "learning_rate": 0.01, "weight_decay": 0.0001, "batch_size": 64, "pmr": 0.0, "random_seed": 4, "client_num_in_total": 50, "client_num_per_round": 50, "momentum": 0.9, "git_commit": "dae6571095d0f6a40abcf753e08a49b6b602d96b"}
+{"round": 95, "test_accuracy": 0.7462, "test_loss": 1.031489, "test_total": 10000, "asr": null, "max_asr": null, "gamma_actual": null, "malicious_count": null, "trigger_value_normalized": [2.5140879154205322, 2.596790313720703, 2.7537312507629395], "agg_time": null, "timestamp": "2026-04-10T20:08:14.984915Z", "aggregator": "shieldfl", "attack_type": "none", "defense_type": "none", "model": "ResNet18", "dataset": "cifar10", "alpha": "100", "runtime_mode": "single-gpu-deterministic", "device": "NVIDIA GeForce RTX 4090", "cuda_version": "12.4", "comm_round": 100, "epochs": 1, "learning_rate": 0.01, "weight_decay": 0.0001, "batch_size": 64, "pmr": 0.0, "random_seed": 4, "client_num_in_total": 50, "client_num_per_round": 50, "momentum": 0.9, "git_commit": "dae6571095d0f6a40abcf753e08a49b6b602d96b"}
+{"round": 96, "test_accuracy": 0.7463, "test_loss": 1.032899, "test_total": 10000, "asr": null, "max_asr": null, "gamma_actual": null, "malicious_count": null, "trigger_value_normalized": [2.5140879154205322, 2.596790313720703, 2.7537312507629395], "agg_time": null, "timestamp": "2026-04-10T20:08:25.286687Z", "aggregator": "shieldfl", "attack_type": "none", "defense_type": "none", "model": "ResNet18", "dataset": "cifar10", "alpha": "100", "runtime_mode": "single-gpu-deterministic", "device": "NVIDIA GeForce RTX 4090", "cuda_version": "12.4", "comm_round": 100, "epochs": 1, "learning_rate": 0.01, "weight_decay": 0.0001, "batch_size": 64, "pmr": 0.0, "random_seed": 4, "client_num_in_total": 50, "client_num_per_round": 50, "momentum": 0.9, "git_commit": "dae6571095d0f6a40abcf753e08a49b6b602d96b"}
+{"round": 97, "test_accuracy": 0.7476, "test_loss": 1.038051, "test_total": 10000, "asr": null, "max_asr": null, "gamma_actual": null, "malicious_count": null, "trigger_value_normalized": [2.5140879154205322, 2.596790313720703, 2.7537312507629395], "agg_time": null, "timestamp": "2026-04-10T20:08:35.868246Z", "aggregator": "shieldfl", "attack_type": "none", "defense_type": "none", "model": "ResNet18", "dataset": "cifar10", "alpha": "100", "runtime_mode": "single-gpu-deterministic", "device": "NVIDIA GeForce RTX 4090", "cuda_version": "12.4", "comm_round": 100, "epochs": 1, "learning_rate": 0.01, "weight_decay": 0.0001, "batch_size": 64, "pmr": 0.0, "random_seed": 4, "client_num_in_total": 50, "client_num_per_round": 50, "momentum": 0.9, "git_commit": "dae6571095d0f6a40abcf753e08a49b6b602d96b"}
+{"round": 98, "test_accuracy": 0.7436, "test_loss": 1.04025, "test_total": 10000, "asr": null, "max_asr": null, "gamma_actual": null, "malicious_count": null, "trigger_value_normalized": [2.5140879154205322, 2.596790313720703, 2.7537312507629395], "agg_time": null, "timestamp": "2026-04-10T20:08:44.541347Z", "aggregator": "shieldfl", "attack_type": "none", "defense_type": "none", "model": "ResNet18", "dataset": "cifar10", "alpha": "100", "runtime_mode": "single-gpu-deterministic", "device": "NVIDIA GeForce RTX 4090", "cuda_version": "12.4", "comm_round": 100, "epochs": 1, "learning_rate": 0.01, "weight_decay": 0.0001, "batch_size": 64, "pmr": 0.0, "random_seed": 4, "client_num_in_total": 50, "client_num_per_round": 50, "momentum": 0.9, "git_commit": "dae6571095d0f6a40abcf753e08a49b6b602d96b"}
+{"round": 99, "test_accuracy": 0.7463, "test_loss": 1.041367, "test_total": 10000, "asr": null, "max_asr": null, "gamma_actual": null, "malicious_count": null, "trigger_value_normalized": [2.5140879154205322, 2.596790313720703, 2.7537312507629395], "agg_time": null, "timestamp": "2026-04-10T20:08:54.701112Z", "aggregator": "shieldfl", "attack_type": "none", "defense_type": "none", "model": "ResNet18", "dataset": "cifar10", "alpha": "100", "runtime_mode": "single-gpu-deterministic", "device": "NVIDIA GeForce RTX 4090", "cuda_version": "12.4", "comm_round": 100, "epochs": 1, "learning_rate": 0.01, "weight_decay": 0.0001, "batch_size": 64, "pmr": 0.0, "random_seed": 4, "client_num_in_total": 50, "client_num_per_round": 50, "momentum": 0.9, "git_commit": "dae6571095d0f6a40abcf753e08a49b6b602d96b"}
diff --git a/doc_fedavg/tools/analyze_baseline.py b/doc_fedavg/tools/analyze_baseline.py
new file mode 100644
index 0000000000..ff1d1eacaa
--- /dev/null
+++ b/doc_fedavg/tools/analyze_baseline.py
@@ -0,0 +1,166 @@
+import json
+from pathlib import Path
+
+import numpy as np
+
+RESULTS_DIR = Path(__file__).resolve().parents[1] / 'n50_results' / 'metrics'
+
+def load_exp(model, dataset, alpha, seed):
+    path = RESULTS_DIR / f'metrics_{model}_{dataset}_shieldfl_atknone_defnone_a{alpha}_pmr0.0_gauto_seed{seed}.jsonl'
+    if not path.exists():
+        return None
+    with path.open(encoding='utf-8') as f:
+        data = [json.loads(l) for l in f]
+    return data
+
+print("=" * 80)
+print("COMPREHENSIVE RESULTS EXTRACTION")
+print("=" * 80)
+
+for ds_label, model, dataset in [("CIFAR-10 + ResNet-18", "ResNet18", "cifar10"),
+                                   ("MNIST + LeNet-5", "LeNet5", "mnist")]:
+    print(f"\n{'='*60}")
+    print(f"  {ds_label}")
+    print(f"{'='*60}")
+    
+    for alpha in ["0.1", "0.5", "100"]:
+        print(f"\n  alpha = {alpha}")
+        all_final = []
+        all_last10_mean = []
+        nan_count = 0
+        
+        for seed in range(5):
+            data = load_exp(model, dataset, alpha, seed)
+            if data is None:
+                print(f"    seed={seed}: MISSING!")
+                continue
+            
+            accs = [d['test_accuracy'] for d in data]
+            losses = [d['test_loss'] for d in data]
+            n_rounds = len(data)
+            
+            has_nan = any(np.isnan(a) for a in accs) or any(np.isnan(l) for l in losses)
+            if has_nan:
+                nan_count += 1
+            
+            final_acc = accs[-1]
+            max_acc = max(accs)
+            max_round = accs.index(max_acc)
+            last10 = accs[-10:]
+            l10_mean = np.mean(last10)
+            l10_std = np.std(last10)
+            
+            if n_rounds >= 20:
+                trend = np.mean(accs[-10:]) - np.mean(accs[-20:-10])
+            else:
+                trend = 0
+            
+            all_final.append(final_acc)
+            all_last10_mean.append(l10_mean)
+            
+            print(f"    seed={seed}: final={final_acc*100:.2f}%  max={max_acc*100:.2f}%@R{max_round}"
+                  f"  last10={l10_mean*100:.2f}%+/-{l10_std*100:.2f}%"
+                  f"  trend={trend*100:+.2f}pp  rounds={n_rounds}  NaN={'YES' if has_nan else 'no'}")
+        
+        if all_final:
+            mean_final = np.mean(all_final) * 100
+            std_final = np.std(all_final) * 100
+            mean_l10 = np.mean(all_last10_mean) * 100
+            print(f"    -- SUMMARY: final={mean_final:.2f}%+/-{std_final:.2f}%"
+                  f"  last10_avg={mean_l10:.2f}%  NaN_seeds={nan_count}/5")
+
+print("\n")
+print("=" * 80)
+print("AC-0-3: SEED DETERMINISM CHECK (C6 = CIFAR-10 a=0.5 seed=0)")
+print("=" * 80)
+
+c6_data = load_exp("ResNet18", "cifar10", "0.5", 0)
+if c6_data:
+    c6_final = c6_data[-1]['test_accuracy'] * 100
+    anchor = 73.31
+    delta = abs(c6_final - anchor)
+    print(f"  C6 final acc: {c6_final:.2f}%")
+    print(f"  Anchor (SA control): {anchor:.2f}%")
+    print(f"  Delta: {delta:.2f}pp")
+    print(f"  AC-0-3 (< 0.5pp): {'PASS' if delta < 0.5 else 'FAIL'}")
+
+print("\n")
+print("=" * 80)
+print("AC-2: STATISTICAL CONSISTENCY")
+print("=" * 80)
+
+for ds_label, model, dataset in [("CIFAR-10", "ResNet18", "cifar10"),
+                                   ("MNIST", "LeNet5", "mnist")]:
+    means = {}
+    for alpha in ["0.1", "0.5", "100"]:
+        finals = []
+        for seed in range(5):
+            data = load_exp(model, dataset, alpha, seed)
+            if data:
+                finals.append(data[-1]['test_accuracy'])
+        means[alpha] = np.mean(finals) if finals else 0
+    
+    mono = means["100"] > means["0.5"] > means["0.1"]
+    print(f"  AC-2-1 {ds_label}: a=100({means['100']*100:.2f}%) > a=0.5({means['0.5']*100:.2f}%) > a=0.1({means['0.1']*100:.2f}%): {'PASS' if mono else 'FAIL'}")
+
+for alpha in ["0.1", "0.5", "100"]:
+    c10_finals = [load_exp("ResNet18", "cifar10", alpha, s)[-1]['test_accuracy'] for s in range(5)]
+    mn_finals = [load_exp("LeNet5", "mnist", alpha, s)[-1]['test_accuracy'] for s in range(5)]
+    c10_mean = np.mean(c10_finals)
+    mn_mean = np.mean(mn_finals)
+    ok = mn_mean > c10_mean
+    print(f"  AC-2-2 a={alpha}: MNIST({mn_mean*100:.2f}%) > CIFAR-10({c10_mean*100:.2f}%): {'PASS' if ok else 'FAIL'}")
+
+print()
+for ds_label, model, dataset in [("CIFAR-10", "ResNet18", "cifar10"),
+                                   ("MNIST", "LeNet5", "mnist")]:
+    for alpha in ["0.1", "0.5", "100"]:
+        finals = [load_exp(model, dataset, alpha, s)[-1]['test_accuracy'] for s in range(5)]
+        std_val = np.std(finals) * 100
+        if dataset == "cifar10":
+            threshold = 3.0 if alpha != "0.1" else 999
+        else:
+            threshold = 2.0
+        
+        if alpha == "0.1" and dataset == "cifar10":
+            status = "N/A (extreme non-IID)"
+        else:
+            status = f"{'PASS' if std_val < threshold else 'FAIL'} (threshold={threshold}pp)"
+        print(f"  AC-2-3 {ds_label} a={alpha}: std={std_val:.2f}pp  {status}")
+
+print()
+for ds_label, model, dataset in [("CIFAR-10", "ResNet18", "cifar10"),
+                                   ("MNIST", "LeNet5", "mnist")]:
+    for alpha in ["0.1", "0.5", "100"]:
+        for seed in range(5):
+            data = load_exp(model, dataset, alpha, seed)
+            accs = [d['test_accuracy'] for d in data]
+            last10_std = np.std(accs[-10:]) * 100
+            status = "PASS" if last10_std < 1.0 else ("WARN" if alpha == "0.1" and dataset == "cifar10" else "FAIL")
+            if last10_std >= 1.0:
+                print(f"  AC-2-4 {ds_label} a={alpha} s={seed}: last10_std={last10_std:.2f}pp  {status}")
+
+print("\n")
+print("=" * 80)
+print("AC-1: ACCURACY RANGE CHECK")
+print("=" * 80)
+
+ac1_ranges = {
+    ("cifar10", "0.1"): (35, 65),
+    ("cifar10", "0.5"): (65, 80),
+    ("cifar10", "100"): (73, 85),
+    ("mnist", "0.1"): (90, 98.5),
+    ("mnist", "0.5"): (94, 99.5),
+    ("mnist", "100"): (96, 99.5),
+}
+
+for ds_label, model, dataset in [("CIFAR-10", "ResNet18", "cifar10"),
+                                   ("MNIST", "LeNet5", "mnist")]:
+    for alpha in ["0.1", "0.5", "100"]:
+        finals = [load_exp(model, dataset, alpha, s)[-1]['test_accuracy'] * 100 for s in range(5)]
+        mean_val = np.mean(finals)
+        lo, hi = ac1_ranges[(dataset, alpha)]
+        in_range = lo <= mean_val <= hi
+        print(f"  AC-1 {ds_label} a={alpha}: mean={mean_val:.2f}%  range=[{lo}%, {hi}%]  {'PASS' if in_range else 'FAIL'}")
+        if not in_range:
+            print(f"    ! Out of range by {max(0, lo-mean_val, mean_val-hi):.2f}pp")
diff --git a/python/examples/federate/prebuilt_jobs/shieldfl/scripts/batch_baseline_n50.sh b/python/examples/federate/prebuilt_jobs/shieldfl/scripts/batch_baseline_n50.sh
new file mode 100644
index 0000000000..01ad640fcc
--- /dev/null
+++ b/python/examples/federate/prebuilt_jobs/shieldfl/scripts/batch_baseline_n50.sh
@@ -0,0 +1,146 @@
+#!/usr/bin/env bash
+# ============================================================================
+# N=50 FedAvg 无攻击基线实验批量执行脚本
+# 实验矩阵：2 数据集 × 3 α × 5 seeds = 30 组
+# 硬件约束：4×RTX 4090，GPU 0 被占用，使用 GPU 1,2,3
+# CIFAR-10 (ResNet18): 3 GPU (1,2,3), 映射 13,19,19, T=100
+# MNIST   (LeNet5)   : 2 GPU (1,2),   映射 26,25,     T=100
+# 参考文档：doc_fedavg/27_[2026-04-11]_N50_FedAvg_无攻击基线实验设计.md
+# 来源整理：PR #2263 `Exp/n50 fedavg baseline 20260411`
+# ============================================================================
+
+set -euo pipefail
+cd "$(dirname "$0")/.."
+
+LOG_DIR="./results/batch_logs"
+DONE_FILE="./results/batch_baseline_done.txt"
+mkdir -p "$LOG_DIR"
+touch "$DONE_FILE"
+
+# 通用参数（§2 冻结参数）
+CLIENTS=50
+EPOCHS=1
+BATCH=64
+MAX_SAMPLES=0
+TEST_SUBSET=0
+LR=0.01
+SERVER_LR=1.0
+GPU_MAPPING=mapping_50clients_isolated
+
+# 无攻击基线固定参数
+ATTACK=none
+DEFENSE=none
+PMR=0.0
+
+# CIFAR-10 参数
+C10_MODEL=ResNet18
+C10_DATASET=cifar10
+C10_ROUNDS=100
+C10_WD=0.0001
+C10_GPU_ID="1,2,3"
+C10_GPU_PROC="13,19,19"
+
+# MNIST 参数（注意 T=100，与攻击实验的 T=50 不同）
+MN_MODEL=LeNet5
+MN_DATASET=mnist
+MN_ROUNDS=100
+MN_WD=0.0
+MN_GPU_ID="1,2"
+MN_GPU_PROC="26,25"
+
+run_one() {
+	local TAG="$1"
+	local MODEL="$2"
+	local DATASET="$3"
+	local ALPHA="$4"
+	local SEED="$5"
+	local ROUNDS="$6"
+	local WD="$7"
+	local GPU_ID="$8"
+	local GPU_PROC="$9"
+
+	# 跳过已完成
+	if grep -qF "$TAG" "$DONE_FILE" 2>/dev/null; then
+		echo "[SKIP] $TAG (already done)"
+		return 0
+	fi
+
+	local LOGF="$LOG_DIR/${TAG}.log"
+	echo ""
+	echo "================================================================"
+	echo "[START] $TAG  $(date '+%Y-%m-%d %H:%M:%S')"
+	echo "================================================================"
+
+	local CMD="bash scripts/run_experiment.sh \
+        --model $MODEL --dataset $DATASET \
+        --attack $ATTACK --defense $DEFENSE \
+        --pmr $PMR --alpha $ALPHA --seed $SEED \
+        --rounds $ROUNDS --clients $CLIENTS \
+        --epochs $EPOCHS --batch_size $BATCH \
+        --max_samples $MAX_SAMPLES --test_subset $TEST_SUBSET \
+        --gpu --gpu_id $GPU_ID \
+        --runtime single-gpu-deterministic \
+        --gpu_mapping $GPU_MAPPING \
+        --gpu_proc_mapping $GPU_PROC \
+        --lr $LR --weight_decay $WD"
+
+	echo "[CMD] $CMD"
+	local T0=$(date +%s)
+
+	if eval "$CMD" >"$LOGF" 2>&1; then
+		local T1=$(date +%s)
+		local DUR=$((T1 - T0))
+		echo "[DONE] $TAG  duration=${DUR}s  $(date '+%Y-%m-%d %H:%M:%S')"
+		echo "$TAG  duration=${DUR}s  $(date '+%Y-%m-%d %H:%M:%S')" >>"$DONE_FILE"
+	else
+		local T1=$(date +%s)
+		local DUR=$((T1 - T0))
+		echo "[FAIL] $TAG  duration=${DUR}s  exit=$?"
+		echo "[FAIL] $TAG  duration=${DUR}s" >>"$DONE_FILE"
+		echo "  → see $LOGF"
+		# 继续下一个实验，不中断
+	fi
+}
+
+echo "========================================"
+echo "  N=50 FedAvg Baseline Runner — $(date)"
+echo "  Total: 30 experiments (no attack)"
+echo "========================================"
+
+# ============================================================================
+# Phase 1: CIFAR-10 基线 (15 组)
+# attack=none, pmr=0.0, α∈{0.1, 0.5, 100}, seed∈{0,1,2,3,4}
+# ============================================================================
+echo ""
+echo "=== Phase 1: CIFAR-10 Baseline (15 groups) ==="
+
+for ALPHA in 0.1 0.5 100; do
+	for SEED in 0 1 2 3 4; do
+		TAG="c10_baseline_a${ALPHA}_s${SEED}"
+		run_one "$TAG" "$C10_MODEL" "$C10_DATASET" \
+			"$ALPHA" "$SEED" \
+			"$C10_ROUNDS" "$C10_WD" "$C10_GPU_ID" "$C10_GPU_PROC"
+	done
+done
+
+# ============================================================================
+# Phase 2: MNIST 基线 (15 组)
+# attack=none, pmr=0.0, α∈{0.1, 0.5, 100}, seed∈{0,1,2,3,4}
+# ============================================================================
+echo ""
+echo "=== Phase 2: MNIST Baseline (15 groups) ==="
+
+for ALPHA in 0.1 0.5 100; do
+	for SEED in 0 1 2 3 4; do
+		TAG="mn_baseline_a${ALPHA}_s${SEED}"
+		run_one "$TAG" "$MN_MODEL" "$MN_DATASET" \
+			"$ALPHA" "$SEED" \
+			"$MN_ROUNDS" "$MN_WD" "$MN_GPU_ID" "$MN_GPU_PROC"
+	done
+done
+
+echo ""
+echo "========================================"
+echo "  Baseline batch complete — $(date)"
+echo "  Results: $DONE_FILE"
+echo "========================================"