mlsanityData to 14 Oct 2026

Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learning

arXiv 2504.12181 ↗First posted 16 Apr 2025cs.LG cs.IT math.ITcited by 2

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Abstract (arXiv)

Federated Learning (FL) has emerged as a promising framework for distributed learning, but its growing complexity has led to significant energy consumption, particularly from computations on the client side. This challenge is especially critical in energy-harvesting FL (EHFL) systems, where device availability fluctuates due to limited and time-varying energy resources. We propose FedBacys, a battery-aware FL framework that introduces cyclic client participation based on users' battery levels to cope with these issues. FedBacys enables clients to save energy and strategically perform local training just before their designated transmission time by clustering clients and scheduling their involvement sequentially. This design minimizes redundant computation, reduces system-wide energy usage, and improves learning stability. Our experiments demonstrate that FedBacys outperforms existing approaches in terms of energy efficiency and performance consistency, exhibiting robustness even under non-i.i.d. training data distributions and with very infrequent battery charging. This work presents the first comprehensive evaluation of cyclic client participation in EHFL, incorporating both communication and computation costs into a unified, resource-aware scheduling strategy.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 16 of its 22 references that are in the map. First posted on arXiv 16 Apr 2025.

Builds on

16 of 22 references.

References · 16

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  3. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  4. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 103
  5. 2023On the Convergence of Federated Averaging with Cyclic Client Participationcited by 31
  6. 2021Federated Learning over Energy Harvesting Wireless Networkscited by 17
  7. 2024Green Federated Learning: A new era of Green Aware AIcited by 12
  8. 2022Over-the-Air Federated Learning with Energy Harvesting Devicescited by 8
  9. 2023Green Federated Learningcited by 8
  10. 2021Energy-Harvesting Distributed Machine Learningcited by 6
  11. 2022Fast-Convergent Federated Learning via Cyclic Aggregationcited by 6
  12. 2024Version age-based client scheduling policy for federated learningcited by 5
4 more
  1. 2024DRACO: Decentralized Asynchronous Federated Learning over Row-Stochastic Wireless Networkscited by 5
  2. 2022On Federated Learning with Energy Harvesting Clientscited by 4
  3. 2024Federated Learning With Energy Harvesting Devices: An MDP Frameworkcited by 4
  4. 2024From AoI to QVAoI: Query-Based Semantics-Aware Scheduling for Energy-Harvesting IoT Systemscited by 1

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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Computation-aware Energy-harvesting Federated Learning with Pipelined Cyclic Schedulingcited by 0
  2. 2025Feature-Based Semantics-Aware Scheduling for Energy-Harvesting Federated Learningcited by 0