Linear Convergent Decentralized Optimization with CompressionDownload PDF

28 Sept 2020, 15:49 (modified: 17 Mar 2021, 22:35)ICLR 2021 PosterReaders: Everyone
Keywords: Decentralized Optimization, Communication Compression, Linear Convergence, Heterogeneous data
Abstract: Communication compression has become a key strategy to speed up distributed optimization. However, existing decentralized algorithms with compression mainly focus on compressing DGD-type algorithms. They are unsatisfactory in terms of convergence rate, stability, and the capability to handle heterogeneous data. Motivated by primal-dual algorithms, this paper proposes the first \underline{L}in\underline{EA}r convergent \underline{D}ecentralized algorithm with compression, LEAD. Our theory describes the coupled dynamics of the inexact primal and dual update as well as compression error, and we provide the first consensus error bound in such settings without assuming bounded gradients. Experiments on convex problems validate our theoretical analysis, and empirical study on deep neural nets shows that LEAD is applicable to non-convex problems.
One-sentence Summary: A Linear Convergent Decentralized Optimization with Communication Compression
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