Divergence-Based Adaptive Aggregation for Byzantine Robust Federated Learning

Summary

This paper proposes two federated learning frameworks, DRAG and BR-DRAG, to counter client drift from data heterogeneity and Byzantine attacks. DRAG uses a reference direction and a divergence-of-degree metric to linearly calibrate each worker's local update without extra communication. BR-DRAG extends this under Byzantine attacks by keeping a vetted root dataset at the server to produce trusted reference directions, and the authors report analytical convergence proofs for non-convex models plus experiments showing superior performance over state-of-the-art methods.

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