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.vscode
.vscode
 
 
__pycache__
__pycache__
 
 
custom_configs
custom_configs
 
 
fedAvg_DANN_configs
fedAvg_DANN_configs
 
 
plotter
plotter
 
 
.gitignore
.gitignore
 
 
DataLoader.py
DataLoader.py
 
 
README.md
README.md
 
 
centralized_DANN.py
centralized_DANN.py
 
 
centralized_ERM.py
centralized_ERM.py
 
 
client.py
client.py
 
 
client_factory.py
client_factory.py
 
 
discriminator.py
discriminator.py
 
 
eval_actor.py
eval_actor.py
 
 
fedAvg_DANN_Scafold_main.py
fedAvg_DANN_Scafold_main.py
 
 
fedAvg_DANN_main.py
fedAvg_DANN_main.py
 
 
fedAvg_main.py
fedAvg_main.py
 
 
federated_data_splitter.py
federated_data_splitter.py
 
 
get_meta_info.py
get_meta_info.py
 
 
google602c4f331ef6c950.html
google602c4f331ef6c950.html
 
 
main.py
main.py
 
 
model.py
model.py
 
 
model_feature_regress.py
model_feature_regress.py
 
 
ray_gpu_smoke.py
ray_gpu_smoke.py
 
 
server.py
server.py
 
 
server.py.old
server.py.old
 
 
server_DANN.py.old
server_DANN.py.old
 
 
server_helper.py
server_helper.py
 
 
strategy_custom.py
strategy_custom.py
 
 
test_ray_actor_min.py
test_ray_actor_min.py
 
 
utils.py
utils.py
 
 

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FedDAPL: Toward Client-Private Generalization in Federated Learning

Official code for the paper “FedDAPL: Toward Client-Private Generalization in Federated Learning.”
📄 Paper: arXiv · 📘 PDF: Direct PDF

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This repository contains the corresponding implementation for the FedDAPL paper, including training scripts, evaluation utilities, and experiment configs.

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Artifact For FedDAPL Paper

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