Zeno++: Robust Fully Asynchronous SGD
Cong Xie, Sanmi Koyejo, Indranil Gupta
摘要
We propose Zeno++, a new robust asynchronous Stochastic Gradient Descent (SGD) procedure, intended to tolerate Byzantine failures of workers. In contrast to previous work, Zeno++ removes several unrealistic restrictions on workerserver communication, now allowing for fully asynchronous updates from anonymous workers, for arbitrarily stale worker updates, and for the possibility of an unbounded number of Byzantine workers. The key idea is to estimate the descent of the loss value after the candidate gradient is applied, where large descent values indicate that the update results in optimization progress. We prove the convergence of Zeno++ for non-convex problems under Byzantine failures. Experimental results show that Zeno++ outperforms existing Byzantine-tolerant asynchronous SGD algorithms.
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引用它的顶会 Paper9
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- BASGD: Buffered Asynchronous SGD for Byzantine LearningYi-Rui Yang, Wu-Jun LiICML 2021 · 被引用 33 次
- High Dimensional Distributed Gradient Descent with Arbitrary Number of Byzantine AttackersWenyu Liu, Tianqiang Huang, Pengfei Zhang, Zong Ke 等AAAI 2026 · 被引用 10 次
- Principled Federated Domain Adaptation: Gradient Projection and Auto-WeightingEnyi Jiang, Yibo Jacky Zhang, Sanmi KoyejoICLR 2024 · 被引用 10 次
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