Communication-efficient SGD algorithms, which allow nodes to perform local updates and periodically synchronize local models, are highly effective in improving the speed and scalability of distributed SGD. However, a rigorous convergence analysis and comparative study of different communication-reduction strategies remains a largely open problem. This paper presents a unified framework called Cooperative SGD that subsumes existing communication-efficient SGD algorithms such as periodic-averaging, elastic-averaging and decentralized SGD. By analyzing Cooperative SGD, we provide novel convergence guarantees for existing algorithms. Moreover, this framework enables us to design new communication-efficient SGD algorithms that strike the best balance between reducing communication overhead and achieving fast error convergence with low error floor.
@article{arxiv.1808.07576,
title = {Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms},
author = {Jianyu Wang and Gauri Joshi},
journal= {arXiv preprint arXiv:1808.07576},
year = {2019}
}