Tight analyses of first-order methods with error feedback

Daniel Berg Thomsen, Adrien Taylor, Aymeric Dieuleveut

Keywords: distributed optimization, distributed learning, error feedback, EF, EF21, tight analysis, performance estimation, convex optimization, large-scale machine learning

Abstract: Communication between agents often constitutes a major computational bottleneck in distributed learning. One of the most common mitigation strategies is to compress the information exchanged, thereby reducing communication overhead. To counteract the degradation in convergence associated with compressed communication, error feedback schemes---most notably EF and EF21---were introduced. In this work, we provide a tight analysis of both of these methods. Specifically, we find the Lyapunov function that yields the best possible convergence rate for each method---with matching lower bounds. This principled approach yields sharp performance guarantees and enables a rigorous, apples-to-apples comparison between EF, EF21, and compressed gradient descent. Our analysis is carried out in the simplified single-agent setting, which allows for clean theoretical insights and fair comparison of the underlying mechanisms.

Primary Area: Optimization (e.g., convex and non-convex, stochastic, robust)

Submission Number: 29188