Local SGD Converges Fast and Communicates Little

TL;DR

This paper proves that local SGD converges at the same rate as mini-batch SGD for convex problems, significantly reducing communication rounds and enabling efficient large-scale distributed training.

Contribution

It provides the first theoretical convergence analysis of local SGD, showing it achieves linear speedup and fewer communication rounds compared to standard methods.

Findings

Local SGD converges at the same rate as mini-batch SGD.

Communication rounds can be reduced up to a factor of T^{1/2}.

Results apply to both convex problems and deep learning models.

Abstract

Mini-batch stochastic gradient descent (SGD) is state of the art in large scale distributed training. The scheme can reach a linear speedup with respect to the number of workers, but this is rarely seen in practice as the scheme often suffers from large network delays and bandwidth limits. To overcome this communication bottleneck recent works propose to reduce the communication frequency. An algorithm of this type is local SGD that runs SGD independently in parallel on different workers and averages the sequences only once in a while. This scheme shows promising results in practice, but eluded thorough theoretical analysis. We prove concise convergence rates for local SGD on convex problems and show that it converges at the same rate as mini-batch SGD in terms of number of evaluated gradients, that is, the scheme achieves linear speedup in the number of workers and mini-batch size. The number of communication rounds can be reduced up to a factor of T^{1/2}---where T denotes the number of total steps---compared to mini-batch SGD. This also holds for asynchronous implementations. Local SGD can also be used for large scale training of deep learning models. The results shown here aim serving as a guideline to further explore the theoretical and practical aspects of local SGD in these applications.