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

2025/01/30 by Furkan Bagci, Büşra Tegin, Bagci, Furkan +5 · 1 citation
Computer Science · Engineering · #Age of Information Optimization #Distributed #Energy Harvesting in Wireless Networks #FOS: Computer and information sciences #Machine Learning (cs.LG) #Parallel #Privacy-Preserving Technologies in Data #and Cluster Computing (cs.DC)

paper · pdf · doi:10.48550/arxiv.2501.18298

openalex publication_date 2025/01/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

We study over-the-air (OTA) federated learning (FL) for energy harvesting devices with heterogeneous data distribution over wireless fading multiple access channel (MAC). To address the impact of low energy arrivals and data heterogeneity on global learning, we propose 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 diverse users, mitigating bias and enhancing convergence. Numerical and analytical results demonstrate improved learning performance by reducing redundancy and conserving energy.

Cited by

• Computation-aware Energy-harvesting Federated Learning: Cyclic Scheduling with Selective Participation

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