Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation

TL;DR

This paper provides a theoretical analysis of server-assisted federated learning (SA-FL) under incomplete client participation, showing its PAC-learnability and proposing a convergent algorithm, SAFARI, that improves performance in practical scenarios.

Contribution

The paper offers the first theoretical justification for SA-FL's effectiveness under incomplete participation and introduces SAFARI, a convergent algorithm with practical guarantees.

Findings

Conventional FL is not PAC-learnable with incomplete participation in worst case.

SA-FL restores PAC-learnability under incomplete participation.

SAFARI achieves linear convergence guarantees similar to ideal FL.

Abstract

Existing works in federated learning (FL) often assume an ideal system with either full client or uniformly distributed client participation. However, in practice, it has been observed that some clients may never participate in FL training (aka incomplete client participation) due to a myriad of system heterogeneity factors. A popular approach to mitigate impacts of incomplete client participation is the server-assisted federated learning (SA-FL) framework, where the server is equipped with an auxiliary dataset. However, despite SA-FL has been empirically shown to be effective in addressing the incomplete client participation problem, there remains a lack of theoretical understanding for SA-FL. Meanwhile, the ramifications of incomplete client participation in conventional FL are also poorly understood. These theoretical gaps motivate us to rigorously investigate SA-FL. Toward this end, we first show that conventional FL is {\em not} PAC-learnable under incomplete client participation in the worst case. Then, we show that the PAC-learnability of FL with incomplete client participation can indeed be revived by SA-FL, which theoretically justifies the use of SA-FL for the first time. Lastly, to provide practical guidance for SA-FL training under {\em incomplete client participation}, we propose the $\mathsf{SAFARI}$ (server-assisted federated averaging) algorithm that enjoys the same linear convergence speedup guarantees as classic FL with ideal client participation assumptions, offering the first SA-FL algorithm with convergence guarantee. Extensive experiments on different datasets show $\mathsf{SAFARI}$ significantly improves the performance under incomplete client participation.