Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks

TL;DR

This paper proposes an adaptive client sampling method for federated learning over wireless networks that optimizes convergence time by considering system and statistical heterogeneity, outperforming baseline schemes.

Contribution

It introduces a new convergence bound for arbitrary client sampling, formulates an adaptive sampling scheme with bandwidth allocation, and develops algorithms to solve the resulting optimization problem.

Findings

The adaptive sampling scheme reduces total convergence time significantly.

Optimal sampling balances the number of clients and per-round time, avoiding excessive sampling.

Experimental results validate the effectiveness of the proposed method over baselines.

Abstract

Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial participation) when the number of participants is large and the server's communication bandwidth is limited. Recent works on the convergence analysis of FL have focused on unbiased client sampling, e.g., sampling uniformly at random, which suffers from slow wall-clock time for convergence due to high degrees of system heterogeneity and statistical heterogeneity. This paper aims to design an adaptive client sampling algorithm for FL over wireless networks that tackles both system and statistical heterogeneity to minimize the wall-clock convergence time. We obtain a new tractable convergence bound for FL algorithms with arbitrary client sampling probability. Based on the bound, we analytically establish the relationship between the total learning time and sampling probability with an adaptive bandwidth allocation scheme, which results in a non-convex optimization problem. We design an efficient algorithm for learning the unknown parameters in the convergence bound and develop a low-complexity algorithm to approximately solve the non-convex problem. Our solution reveals the impact of system and statistical heterogeneity parameters on the optimal client sampling design. Moreover, our solution shows that as the number of sampled clients increases, the total convergence time first decreases and then increases because a larger sampling number reduces the number of rounds for convergence but results in a longer expected time per-round due to limited wireless bandwidth. Experimental results from both hardware prototype and simulation demonstrate that our proposed sampling scheme significantly reduces the convergence time compared to several baseline sampling schemes.