Update estimation and scheduling for over-the-air federated learning with energy harvesting devices
File(s)
Accepted version
Author(s)
Bagci, Furkan
Tegin, Busra
Kazemi, Mohammad
Duman, Tolga M
Type
Conference Paper
Abstract
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.
Date Issued
2025-09-22
Date Acceptance
2025-06-01
Citation
2025 IEEE International Conference on Communications Workshops (ICC Workshops), 2025, pp.1574-1579
Publisher
IEEE
Start Page
1574
End Page
1579
Journal / Book Title
2025 IEEE International Conference on Communications Workshops (ICC Workshops)
Copyright Statement
Copyright © 2025, IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Source
2025 IEEE International Conference on Communications Workshops (ICC Workshops)
Publication Status
Published
Start Date
2025-06-08
Finish Date
2025-06-12
Coverage Spatial
Montreal, QC, Canada