REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning over Mobile Devices

TL;DR

REWAFL introduces a novel participant selection method for federated learning that considers residual energy and wireless conditions, improving training efficiency and device battery health over mobile networks.

Contribution

It proposes a joint utility function for participant selection that integrates energy and wireless factors, addressing residual energy and staleness issues in federated learning.

Findings

Improves training accuracy and efficiency in FL.

Avoids rapid battery depletion of mobile devices.

Effectively manages wireless heterogeneity impacts.

Abstract

Participant selection (PS) helps to accelerate federated learning (FL) convergence, which is essential for the practical deployment of FL over mobile devices. While most existing PS approaches focus on improving training accuracy and efficiency rather than residual energy of mobile devices, which fundamentally determines whether the selected devices can participate. Meanwhile, the impacts of mobile devices' heterogeneous wireless transmission rates on PS and FL training efficiency are largely ignored. Moreover, PS causes the staleness issue. Prior research exploits isolated functions to force long-neglected devices to participate, which is decoupled from original PS designs. In this paper, we propose a residual energy and wireless aware PS design for efficient FL training over mobile devices (REWAFL). REW AFL introduces a novel PS utility function that jointly considers global FL training utilities and local energy utility, which integrates energy consumption and residual battery energy of candidate mobile devices. Under the proposed PS utility function framework, REW AFL further presents a residual energy and wireless aware local computing policy. Besides, REWAFL buries the staleness solution into its utility function and local computing policy. The experimental results show that REW AFL is effective in improving training accuracy and efficiency, while avoiding "flat battery" of mobile devices.