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

TL;DR

This paper introduces Cooperative SGD, a unified framework that analyzes and designs communication-efficient distributed SGD algorithms, improving scalability and convergence guarantees while balancing communication costs.

Contribution

The paper provides a unified analysis of existing communication-efficient SGD algorithms and proposes new algorithms with optimized communication and convergence properties.

Findings

Unified convergence guarantees for multiple algorithms

Design of new communication-efficient SGD algorithms

Balance between communication reduction and convergence speed

Abstract

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.