Towards Energy-Aware Federated Learning on Battery-Powered Clients

TL;DR

This paper introduces EAFL, an energy-aware federated learning method that selects battery-rich clients to improve training efficiency, accuracy, and client retention on battery-powered devices.

Contribution

The paper proposes EAFL, a novel energy-aware client selection algorithm for federated learning that considers battery levels to enhance participation and system performance.

Findings

EAFL improves model accuracy by up to 85%.

EAFL reduces client drop-out by up to 2.45 times.

EAFL effectively balances training efficiency and energy consumption.

Abstract

Federated learning (FL) is a newly emerged branch of AI that facilitates edge devices to collaboratively train a global machine learning model without centralizing data and with privacy by default. However, despite the remarkable advancement, this paradigm comes with various challenges. Specifically, in large-scale deployments, client heterogeneity is the norm which impacts training quality such as accuracy, fairness, and time. Moreover, energy consumption across these battery-constrained devices is largely unexplored and a limitation for wide-adoption of FL. To address this issue, we develop EAFL, an energy-aware FL selection method that considers energy consumption to maximize the participation of heterogeneous target devices. EAFL is a power-aware training algorithm that cherry-picks clients with higher battery levels in conjunction with its ability to maximize the system efficiency. Our design jointly minimizes the time-to-accuracy and maximizes the remaining on-device battery levels. EAFLimproves the testing model accuracy by up to 85\% and decreases the drop-out of clients by up to 2.45$\times$.