{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/byzantine-robust-decentralized-learning-via","title":"Byzantine-Robust Decentralized Learning via ClippedGossip","arxiv_id":"2202.01545","date":"2022-02-03","proceeding":null,"authors":["Lie He","Sai Praneeth Karimireddy","Martin Jaggi"],"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. 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