Zeno++: Robust Fully Asynchronous SGD
March 17, 2019 ยท Declared Dead ยท ๐ International Conference on Machine Learning
"No code URL or promise found in abstract"
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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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