Eecs-fl: energy-efficient client selection for federated learning in AIoT

Abstract

Abstract The Artificial Intelligence of Things (AIoT) ecosystem faces significant challenges related to limited client energy budgets and resource heterogeneity, particularly when employing the Federated Learning (FL) framework. This paper presents a novel energy-efficient client selection algorithm for FL, designed to address these challenges by integrating Wireless Power Transfer (WPT), where WPT involves in the client selection optimization, based on real-time energy availability and resource heterogeneity. We formulate the client selection problem as a multi-dimensional knapsack problem (MKP) and solve it using dynamic programming to maximize energy efficiency while maintaining fast convergence. Experimental results show that incorporating WPT leads to a reduction in unit energy consumption by over 24.54%; while, the proposed algorithm achieves a reduction of over 15.31% compared to random selection. The proposed approach improves energy utilization, demonstrates strong resilience to client heterogeneity, and adapts efficiently to varying energy supply conditions.