[Paper] Enhancing Robustness of Federated Learning via Server Learning

Summary

This paper proposes a novel approach to enhance the robustness of federated learning against malicious attacks, even when clients' training data is not independently and identically distributed. The method combines server learning, client update filtering, and geometric median aggregation into a heuristic algorithm. Experiments demonstrate that this technique significantly improves model accuracy, even when a high fraction of clients are malicious.

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