INNER CODE UNIT · Python

seed

Tisha-runwal/Personalized-Federated-Learning-for-Privacy--Preserving-and-Scalable-IoT-Driven-Smart-Healthcare · pfl_hcare/fl/server.py:189

                seed=seed + rnd,
            )
        else:
            selected_ids = list(range(num_clients))

        for cid in selected_ids:
            client_last_participation[cid] = rnd

        # ---------------------------------------------------------------
        # Step 2: Local Fit (MAML Eq.3-4, DP Eq.5 applied client-side)
        # ---------------------------------------------------------------
        fit_results = []
        for cid in selected_ids:
            client = clients[cid]
            updated, n_samples, fit_metrics = client.fit(
                parameters=[arr.copy() for arr in global_params],
                config={"round": rnd},
            )

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