Byzantine-Robust Decentralized Learning via ClippedGossip

• URL: http://arxiv.org/abs/2202.01545v2
• Date: Thu, 20 Apr 2023 15:22:15 GMT
• Title: Byzantine-Robust Decentralized Learning via ClippedGossip
• Authors: Lie He, Sai Praneeth Karimireddy, Martin Jaggi
• Abstract summary: We propose a ClippedGossip algorithm for Byzantine-robust consensus optimization. We demonstrate the encouraging empirical performance of ClippedGossip under a large number of attacks.
• Score: 61.03711813598128
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we study the challenging task of Byzantine-robust decentralized training on arbitrary communication graphs. Unlike federated learning where workers communicate through a server, workers in the decentralized environment can only talk to their neighbors, making it harder to reach consensus and benefit from collaborative training. To address these issues, we propose a ClippedGossip algorithm for Byzantine-robust consensus and optimization, which is the first to provably converge to a $O(\delta_{\max}\zeta^2/\gamma^2)$ neighborhood of the stationary point for non-convex objectives under standard assumptions. Finally, we demonstrate the encouraging empirical performance of ClippedGossip under a large number of attacks.

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