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Communication-efficient SGD algorithms, which allow nodes to perform localupdates and periodically synchronize local models, are highly effective inimproving the speed and scalability of distributed SGD. However, a rigorousconvergence analysis and comparative study of different communication-reductionstrategies remains a largely open problem. This paper presents a unifiedframework called Cooperative SGD that subsumes existing communication-efficientSGD algorithms such as periodic-averaging, elastic-averaging and decentralizedSGD. By analyzing Cooperative SGD, we provide novel convergence guarantees forexisting algorithms. Moreover, this framework enables us to design newcommunication-efficient SGD algorithms that strike the best balance betweenreducing communication overhead and achieving fast error convergence with lowerror floor.

Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms