Communication-Compressed Adaptive Gradient Method for Distributed Nonconvex Optimization

TL;DR

This paper introduces a communication-efficient distributed adaptive gradient method, AMSGrad, with provable convergence guarantees for nonconvex optimization, reducing communication costs while maintaining performance.

Contribution

It presents a novel communication-compressed AMSGrad algorithm with theoretical convergence guarantees for distributed nonconvex optimization.

Findings

Converges to first-order stationary points at the same rate as uncompressed AMSGrad.

Effective gradient compression strategy reduces communication costs.

Experimental results validate theoretical claims.

Abstract

Due to the explosion in the size of the training datasets, distributed learning has received growing interest in recent years. One of the major bottlenecks is the large communication cost between the central server and the local workers. While error feedback compression has been proven to be successful in reducing communication costs with stochastic gradient descent (SGD), there are much fewer attempts in building communication-efficient adaptive gradient methods with provable guarantees, which are widely used in training large-scale machine learning models. In this paper, we propose a new communication-compressed AMSGrad for distributed nonconvex optimization problem, which is provably efficient. Our proposed distributed learning framework features an effective gradient compression strategy and a worker-side model update design. We prove that the proposed communication-efficient distributed adaptive gradient method converges to the first-order stationary point with the same iteration complexity as uncompressed vanilla AMSGrad in the stochastic nonconvex optimization setting. Experiments on various benchmarks back up our theory.