BatteryFL: Battery-Aware Federated Learning

Abstract

Federated learning (FL) has emerged as a transformative paradigm enabling collaborative machine learning without centralizing data, preserving client privacy. This is particularly relevant in the context of edge computing, where the proliferation of Internet of Things devices has led to an explosion of data at the network’s edge. These IoT devices, often battery-powered, are limited by their energy capacities, which pose significant challenges for the adoption of FL in such environments. In this paper, we introduce BatteryFL, a novel framework that coordinates battery-aware clients through FL to maximize their contribution to the global model while ensuring a fair distribution of energy consumption across the clients without compromising accuracy. BatteryFL incorporates an innovative data collection algorithm that prioritizes data diversity to minimize battery usage and a sample relevance-based algorithm to select optimal data for training. We also integrate a client selection strategy into the framework to optimize training loss and fairness (based on the battery energy of the clients) simultaneously. Along with a theoretical analysis, we experimentally demonstrate that BatteryFL significantly improves the energy efficiency of FL, prolonging the data collection and the contributions of the clients.