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

• URL: http://arxiv.org/abs/2510.07922v2
• Date: Fri, 10 Oct 2025 02:39:50 GMT
• Title: SketchGuard: Scaling Byzantine-Robust Decentralized Federated Learning via Sketch-Based Screening
• Authors: Murtaza Rangwala, Farag Azzedin, Richard O. Sinnott, Rajkumar Buyya,
• Abstract summary: Decentralized Federated Learning (DFL) enables privacy-preserving collaborative training without centralized servers.<n>DFL is vulnerable to Byzantine attacks where malicious clients update corrupted model.<n>We propose SketchGuard to decouple Byzantine filtering from model aggregation through sketch-based neighbor screening.
• Score: 15.287835378843425
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Decentralized Federated Learning (DFL) enables privacy-preserving collaborative training without centralized servers, but remains vulnerable to Byzantine attacks where malicious clients submit corrupted model updates. Existing Byzantine-robust DFL defenses rely on similarity-based neighbor screening that requires every client to exchange and compare complete high-dimensional model vectors with all neighbors in each training round, creating prohibitive communication and computational costs that prevent deployment at web scale. We propose SketchGuard, a general framework that decouples Byzantine filtering from model aggregation through sketch-based neighbor screening. SketchGuard compresses $d$-dimensional models to $k$-dimensional sketches ($k \ll d$) using Count Sketch for similarity comparisons, then selectively fetches full models only from accepted neighbors, reducing per-round communication complexity from $O(d|N_i|)$ to $O(k|N_i| + d|S_i|)$, where $|N_i|$ is the neighbor count and $|S_i| \le |N_i|$ is the accepted neighbor count. We establish rigorous convergence guarantees in both strongly convex and non-convex settings, proving that Count Sketch compression preserves Byzantine resilience with controlled degradation bounds where approximation errors introduce only a $(1+O(\epsilon))$ factor in the effective threshold parameter. Comprehensive experiments across multiple datasets, network topologies, and attack scenarios demonstrate that SketchGuard maintains identical robustness to state-of-the-art methods while reducing computation time by up to 82% and communication overhead by 50-70% depending on filtering effectiveness, with benefits scaling multiplicatively with model dimensionality and network connectivity. These results establish the viability of sketch-based compression as a fundamental enabler of robust DFL at web scale.

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