AMF-CFL: Anomaly model filtering based on clustering in federated learning

Summary

The paper proposes AMF-CFL, a robust aggregation algorithm for federated learning under non-i.i.d. client data distributions. It filters malicious client updates in two steps, first with multi-k-means clustering and then with z-score-based statistical analysis. The authors report that it preserves global model integrity against four untargeted and two targeted attack types.

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