A federated server that weights client updates by the inverse of each model's validation loss on a small proxy dataset outperforms data-size weighting, but its convergence proof assumes the weight concentration that the method is supposed to provide.
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FedLBW: A Loss-Based Weighting Strategy for Federated Learning on Non-IID Data in Wireless Networks
A federated server that weights client updates by the inverse of each model's validation loss on a small proxy dataset outperforms data-size weighting, but its convergence proof assumes the weight concentration that the method is supposed to provide.