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• W3044893339 abstract "The scalability of Distributed Stochastic Gradient Descent (SGD) is today limited by communication bottlenecks. We propose a novel SGD variant: Communication-efficient SGD with Error Reset, or CSER. The key idea in CSER is first a new technique called error reset that adapts arbitrary compressors for SGD, producing bifurcated local models with periodic reset of resulting local residual errors. Second we introduce partial synchronization for both the gradients and the models, leveraging advantages from them. We prove the convergence of CSER for smooth non-convex problems. Empirical results show that when combined with highly aggressive compressors, the CSER algorithms accelerate the distributed training by nearly 10x for CIFAR-100, and by 4.5x for ImageNet." @default.
• W3044893339 created "2020-07-29" @default.
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• W3044893339 date "2020-07-26" @default.
• W3044893339 modified "2023-09-25" @default.
• W3044893339 title "CSER: Communication-efficient SGD with Error Reset" @default.
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• W3044893339 doi "https://doi.org/10.48550/arxiv.2007.13221" @default.
• W3044893339 hasPublicationYear "2020" @default.
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