Energy-Aware Bid-Based Client Selection for Federated Learning in Resource-Constrained IoT Networks
Rana Albelaihi · 2026
Federated learning (FL) enables distributed IoT devices to train machine learning models collaboratively without sharing raw data. However, energy heterogeneity among devices significantly challenges efficient and equitable participation, particularly in resource-constrained networks. This paper introduces BEAF (Bid-based Energy-Aware Federated Learning), a client selection strategy that incorporates the availability of energy and the training utility of the device into a unified selection criterion. Each client independently computes a bid score based on its remaining energy and the relative improvement in local training loss. Clients with the highest utility-per-joule scores are selected to participate in each round. The approach operates without centralized profiling or historical coordination and is compatible with synchronous FL protocols. The evaluation of standard benchmarks shows that BEAF enhances the precision of the global model, reduces total energy consumption, and improves fairness in client participation compared to baseline methods, such as random sampling and selection based on energy thresholds. The method is suitable for deployment in energy-limited environments, including agricultural monitoring and other distributed sensing applications.