mlsanityData to 14 Oct 2026

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

arXiv 2501.18298 ↗First posted 30 Jan 2025cs.LG cs.DCcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 12 of its 22 references that are in the map. First posted on arXiv 30 Jan 2025.

Builds on

12 of 22 references.

References · 12

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  3. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  4. 2018Federated Learning with Non-IID Datacited by 1,108
  5. 2021Federated Learning Based on Dynamic Regularizationcited by 562
  6. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 252
  7. 2018Federated Learning via Over-the-Air Computationcited by 219
  8. 2021Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learningcited by 93
  9. 2020Blind Federated Edge Learningcited by 43
  10. 2022Over-the-Air Federated Edge Learning with Hierarchical Clusteringcited by 10
  11. 2022Over-the-Air Federated Learning with Energy Harvesting Devicescited by 8
  12. 2024Federated Learning With Energy Harvesting Devices: An MDP Frameworkcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Computation-aware Energy-harvesting Federated Learning with Pipelined Cyclic Schedulingcited by 0
  2. 2026Cluster-Aware Over-the-Air Federated Learning with Energy-Harvesting Devices: From Global Training to Model Personalizationcited by 0