Energy-Efficient Federated Learning: Integrating Model Pruning, Compressive Sensing, and Outage Compensation
Fangming Guan, Xiangwang Hou, Xianghe Wang, Jingjing Wang, Jun Du, Yong Ren · 2025
The rapid advancement of technologies such as the Internet of Things (IoT), autonomous driving, and smart manufacturing has led to a massive increase in data generation at the edge of networks. This necessitates effective machine learning (ML) methods that address challenges like communication overhead and privacy concerns. Federated learning (FL) has emerged as a promising solution for distributed model training, but the increasing complexity of ML models limits its communication efficiency. To address these challenges, we propose an ultra energy-efficient FL framework (FedUEE). FedUEE utilizes model pruning-based compressive sensing, outage compensation, and joint optimization of learning and resource configurations to comprehensively reduce energy consumption. We develop analytical models that quantify the energy impact of each proposed mechanism, ultimately providing an optimized solution for communication efficiency in edge FL environments.