Update Estimation and Scheduling for Over-the-Air Federated Learning with Energy Harvesting Devices

We study over-the-air federated learning for energy harvesting devices with heterogeneous data distribution over wireless fading multiple access channels. To address the impact of low energy arrivals and data heterogeneity on global learning, we propose different user scheduling strategies. Specifically, we develop two approaches: 1) entropy-based scheduling for known data distributions, and 2) least-squares-based user representation estimation for scheduling with unknown data distributions at the parameter server. Both methods aim to select a diverse set of users to participate in the learning process, mitigating bias and enhancing convergence behavior. Numerical and analytical results demonstrate improved learning performance.