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arXiv Machine Learning By Murtaza Rangwala, Farag Azzedin, Richard O. Sinnott, Rajkumar Buyya

SketchGuard: Scaling Byzantine-Robust Decentralized Federated Learning via Sketch-Based Screening

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SketchGuard is a Byzantine‑robust decentralized federated learning method that separates neighbor screening from model aggregation by using a Count Sketch representation. The approach mitigates a vulnerability where an adaptive adversary can hide large perturbations in the sketch’s null space, by adopting a commit‑then‑sketch protocol that ensures the sketch seed is chosen only after model commitment. The authors prove convergence in both convex and non‑convex settings, demonstrate that SketchGuard achieves state‑of‑the‑art robustness against six attacks—including the adaptive null‑space attack—across various network topologies and data heterogeneity, while reducing per‑neighbor communication to a model‑dimension‑independent size.

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