Improving Accelerated Federated Learning with Compression and Importance Sampling

TL;DR

This paper presents a comprehensive federated learning method combining local training, compression, and importance sampling for partial participation, achieving state-of-the-art convergence guarantees and practical improvements.

Contribution

It introduces a complete federated learning framework integrating all key techniques and derives an importance sampling scheme for enhanced performance.

Findings

Achieves state-of-the-art convergence guarantees.

Importance sampling improves performance.

Experimental results confirm practical advantages.

Abstract

Federated Learning is a collaborative training framework that leverages heterogeneous data distributed across a vast number of clients. Since it is practically infeasible to request and process all clients during the aggregation step, partial participation must be supported. In this setting, the communication between the server and clients poses a major bottleneck. To reduce communication loads, there are two main approaches: compression and local steps. Recent work by Mishchenko et al. [2022] introduced the new ProxSkip method, which achieves an accelerated rate using the local steps technique. Follow-up works successfully combined local steps acceleration with partial participation [Grudzie\'n et al., 2023, Condat et al. 2023] and gradient compression [Condat et al. [2022]. In this paper, we finally present a complete method for Federated Learning that incorporates all necessary ingredients: Local Training, Compression, and Partial Participation. We obtain state-of-the-art convergence guarantees in the considered setting. Moreover, we analyze the general sampling framework for partial participation and derive an importance sampling scheme, which leads to even better performance. We experimentally demonstrate the advantages of the proposed method in practice.