INNER CODE UNIT · Python

n_select

Tisha-runwal/Personalized-Federated-Learning-for-Privacy--Preserving-and-Scalable-IoT-Driven-Smart-Healthcare · pfl_hcare/fl/server.py:184

                n_select=max(2, num_clients // 2),
                gradient_norms=client_gradient_norms,
                last_participation=client_last_participation,
                current_round=rnd,
                min_interval=min_participation_interval,
                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:

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