Byzantine-robust Decentralized Learning via ClippedGossip

Lie He, Sai Praneeth Karimireddy, Martin Jaggi

Keywords: Byzantine-robustness, distributed machine learning, robustness, optimization, decentralized learning

TL;DR: In this paper, we study the challenging task of Byzantine-robust decentralized training on arbitrary communication graphs.

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 an algorithm, termed ClippedGossip, 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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