client_1.py

from flwr.client.numpy_client import NumPyClient
from task import load_model, load_data
import flwr as fl

class FlowerClient(NumPyClient):
    def __init__(self, model, data, epochs, batch_size, verbose):
        self.model = model
        self.x_train, self.y_train, self.x_test, self.y_test = data
        self.epochs = epochs
        self.batch_size = batch_size
        self.verbose = verbose

    def get_parameters(self, config):
        """Return the current model parameters."""
        return self.model.get_weights()

    def set_parameters(self, parameters):
        """Set the model parameters."""
        self.model.set_weights(parameters)

    def fit(self, parameters, config):
        """Fit the model on local data."""
        self.set_parameters(parameters)
        self.model.fit(self.x_train, self.y_train, epochs=self.epochs, batch_size=self.batch_size, verbose=self.verbose)
        return self.model.get_weights(), len(self.x_train), {}

    def evaluate(self, parameters, config):
        """Evaluate the model on the local test data."""
        self.set_parameters(parameters)
        loss, accuracy = self.model.evaluate(self.x_test, self.y_test, verbose=0)
        return loss, len(self.x_test), {"accuracy": accuracy}

# Load data for client 1 (partition 0)
data = load_data(partition_id=0, num_partitions=2)
model = load_model()

# Run the client using the new `start_client()` method
fl.client.start_client(
    server_address="localhost:8080",
    client=FlowerClient(model, data, epochs=1, batch_size=32, verbose=1).to_client()
)