Zeno++: Robust Fully Asynchronous SGD

March 17, 2019 ยท Declared Dead ยท ๐Ÿ› International Conference on Machine Learning

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Authors Cong Xie, Sanmi Koyejo, Indranil Gupta arXiv ID 1903.07020 Category cs.LG: Machine Learning Cross-listed cs.DC, stat.ML Citations 129 Venue International Conference on Machine Learning Last Checked 3 months ago
Abstract
We propose Zeno++, a new robust asynchronous Stochastic Gradient Descent~(SGD) procedure which tolerates Byzantine failures of the workers. In contrast to previous work, Zeno++ removes some unrealistic restrictions on worker-server communications, allowing for fully asynchronous updates from anonymous workers, arbitrarily stale worker updates, and 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 approaches.
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