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1 Introduction Federated learning (FL) [1] enables distributed clients (e.g., edge devices in IoT networks) to collaboratively train a shared model by exchanging model parameters while keeping raw training data local. In heterogeneous environments, local updates reflect the optimization tendencies associated with each client's data distribution [2-4].
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