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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 worker-server 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.
Author Information
Cong Xie (UIUC)
Sanmi Koyejo (Illinois / Google)
Sanmi (Oluwasanmi) Koyejo an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Koyejo's research interests are in the development and analysis of probabilistic and statistical machine learning techniques motivated by, and applied to various modern big data problems. He is particularly interested in the analysis of large scale neuroimaging data. Koyejo completed his Ph.D in Electrical Engineering at the University of Texas at Austin advised by Joydeep Ghosh, and completed postdoctoral research at Stanford University with a focus on developing Machine learning techniques for neuroimaging data. His postdoctoral research was primarily with Russell A. Poldrack and Pradeep Ravikumar. Koyejo has been the recipient of several awards including the outstanding NCE/ECE student award, a best student paper award from the conference on uncertainty in artificial intelligence (UAI) and a trainee award from the Organization for Human Brain Mapping (OHBM).
Indranil Gupta (UIUC)
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2020 Poster: On the consistency of top-k surrogate losses »
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2019 Oral: Partially Linear Additive Gaussian Graphical Models »
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2019 Oral: Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance »
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2018 Poster: Binary Classification with Karmic, Threshold-Quasi-Concave Metrics »
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2018 Oral: Binary Classification with Karmic, Threshold-Quasi-Concave Metrics »
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2017 Poster: Consistency Analysis for Binary Classification Revisited »
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