Do We Need Asynchronous SGD? On the Near-Optimality of Synchronous Methods

2026-02-03

AI Summary

This work re-examines the necessity of asynchronous stochastic gradient descent (SGD) in distributed optimization by rigorously analyzing synchronous SGD and its robust variant, m-synchronous SGD, under a realistic heterogeneous setting that combines stochastic computation delays with adversarial partial participation. Through theoretical analysis, the authors demonstrate that the time complexity of these synchronous methods deviates from the optimal only by a logarithmic factor. By introducing a more practical heterogeneity model that unifies both random and adversarial elements, the study systematically evaluates the convergence efficiency of synchronous approaches and reveals their near-optimality across a broad range of heterogeneous scenarios. These findings challenge the prevailing assumption that asynchronous methods are indispensable for efficient distributed optimization in heterogeneous environments.

Technology Category

• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Large-scale security measurements

Abstract

Modern distributed optimization methods mostly rely on traditional synchronous approaches, despite substantial recent progress in asynchronous optimization. We revisit Synchronous SGD and its robust variant, called $m$-Synchronous SGD, and theoretically show that they are nearly optimal in many heterogeneous computation scenarios, which is somewhat unexpected. We analyze the synchronous methods under random computation times and adversarial partial participation of workers, and prove that their time complexities are optimal in many practical regimes, up to logarithmic factors. While synchronous methods are not universal solutions and there exist tasks where asynchronous methods may be necessary, we show that they are sufficient for many modern heterogeneous computation scenarios.

Problem

• Asynchronous SGD
• Synchronous SGD
• Distributed Optimization
• Heterogeneous Computation
• Time Complexity

Innovation

• Synchronous SGD
• m-Synchronous SGD
• heterogeneous computation
• near-optimality
• distributed optimization