Toward Sustainable Federated Learning: A Learning Incentive Framework Balancing Energy Limits and Fairness

Abstract

Federated Learning (FL) has gained significant attention for its ability to collaboratively train machine learning models across distributed clients. However, traditional FL approaches often overlook the issues of both participation willingness of clients and their early dropouts due to energy depletions over a long-term training. This will result in insufficient training data in FL, and further weaken model generalizations. In this paper, by considering both energy limits and fairness selection of clients, we propose an iterative Auction and Reinforcement Learning (RL) enabled sustainable FL, i.e., SFARL, in which a sustainable RL, i.e., FSRL, is designed to satisfy clients' energy limits by determining the optimal local training iterations. To promote fairness among clients, a client selection algorithm and a two-part payment rule are further designed. The theoretical analysis demonstrates that SFARL upholds incentive compatibility, individual rationality, and fairness. Experimental results show that SFARL consistently achieves top-tier accuracy on MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100, while preserving the largest number of surviving clients and the lowest fairness metric among the compared methods. In addition, SFARL exhibits the best energy utilization efficiency and the one-time reporting mechanism significantly reduces the overall system consumption.