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import flwr as fl |
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import numpy as np |
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import json |
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import pandas as pd |
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from model import create_model |
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import sys |
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results_log = [] |
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def get_evaluate_fn(file_path): |
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data = pd.read_csv(file_path) |
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X = data.iloc[:, :-1].values |
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y = data.iloc[:, -1].values |
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X = (X - X.mean(axis=0)) / (X.std(axis=0) + 1e-8) |
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split = int(len(X) * 0.8) |
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X_test, y_test = X[split:], y[split:] |
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input_shape = X.shape[1] |
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if "diabetes" in file_path: |
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model_name = "diabetes_model.keras" |
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elif "heart" in file_path: |
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model_name = "heart_model.keras" |
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else: |
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model_name = "global_model.keras" |
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def evaluate(server_round, parameters, config): |
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model = create_model(input_shape=input_shape) |
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model.set_weights(parameters) |
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loss, accuracy = model.evaluate(X_test, y_test, verbose=0) |
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results_log.append({ |
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"round": server_round, |
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"loss": round(loss, 4), |
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"accuracy": round(accuracy, 4) |
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}) |
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with open("training_results.json", "w") as f: |
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json.dump(results_log, f) |
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if server_round == 3: |
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model.save(model_name) |
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print(f"Model saved as {model_name}") |
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return loss, {"accuracy": accuracy} |
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return evaluate |
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def main(file_path="diabetes.csv"): |
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global results_log |
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results_log = [] |
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with open("training_results.json", "w") as f: |
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json.dump([], f) |
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strategy = fl.server.strategy.FedAvg( |
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min_fit_clients=2, |
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min_evaluate_clients=2, |
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min_available_clients=2, |
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evaluate_fn=get_evaluate_fn(file_path), |
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) |
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fl.server.start_server( |
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server_address="localhost:8080", |
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config=fl.server.ServerConfig(num_rounds=3), |
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strategy=strategy, |
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) |
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if __name__ == "__main__": |
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file_path = sys.argv[1] if len(sys.argv) > 1 else "diabetes.csv" |
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main(file_path) |
