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Computation-aware Energy-harvesting Federated Learning with Pipelined Cyclic Scheduling

arXiv 2511.11949 ↗First posted 14 Nov 2025cs.LG cs.IT math.ITcited by 0

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Abstract (arXiv)

Federated learning (FL) is a powerful paradigm for distributed learning, but increasing model complexity leads to significant energy consumption from client-side computations for local training. This challenge is critical in energy-harvesting FL (EHFL) systems, where the participation availability of each device fluctuates because of limited energy. To address this, we propose PipeCycle, a battery-aware distributed learning framework that organizes clients into pipelined cyclic groups. When a group completes its intra-group aggregation, its aggregated model is relayed directly to a newly formed group as a reference for local training, allowing multiple groups to coexist in the pipeline while overlapping client recharging periods with active training in other pipeline stages. We provide a convergence analysis of PipeCycle under a realistic energy consumption model in which local training spans multiple time slots, and show that the cyclic structure of the pipeline imposes a finite-horizon staleness bound that avoids the exponential factors typical of asynchronous FL analyses. Numerical experiments across both IID and non-IID data and various battery charging probabilities show that PipeCycle reaches a target accuracy with substantially lower cumulative energy than existing FL baselines, particularly under severe label skew where competing cyclic schemes collapse to near-chance accuracy.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 29 of its 51 references that are in the map. First posted on arXiv 14 Nov 2025.

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29 of 51 references.

References · 29

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
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  3. 2020An Efficient Framework for Clustered Federated Learningcited by 505
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  5. 2021Fast Federated Learning in the Presence of Arbitrary Device Unavailabilitycited by 72
  6. 2022Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delayscited by 61
  7. 2023On the Convergence of Federated Averaging with Cyclic Client Participationcited by 31
  8. 2022Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraintscited by 30
  9. 2020Dynamic Clustering in Federated Learningcited by 27
  10. 2021Federated Learning over Energy Harvesting Wireless Networkscited by 17
  11. 2022Wireless Quantized Federated Learning: A Joint Computation and Communication Designcited by 15
  12. 2025FedALT: Federated Fine-Tuning through Adaptive Local Training with Rest-of-World LoRAcited by 13
17 more
  1. 2024Green Federated Learning: A new era of Green Aware AIcited by 12
  2. 2023FedZero: Leveraging Renewable Excess Energy in Federated Learningcited by 11
  3. 2023AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devicescited by 10
  4. 2022Knowledge-aided Federated Learning for Energy-limited Wireless Networkscited by 8
  5. 2023Green Federated Learningcited by 8
  6. 2024Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Driftcited by 8
  7. 2024Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysiscited by 8
  8. 2024Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approachcited by 6
  9. 2022On Federated Learning with Energy Harvesting Clientscited by 4
  10. 2022Communication-Efficient Local SGD with Age-Based Worker Selectioncited by 3
  11. 2025Energy, Scalability, Data and Security in Massive IoT: Current Landscape and Future Directionscited by 3
  12. 2021Simultaneous Wireless Information and Power Transfer for Federated Learningcited by 2
  13. 2025Update Estimation and Scheduling for Over-the-Air Federated Learning with Energy Harvesting Devicescited by 2
  14. 2025Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learningcited by 2
  15. 2025Federated Learning-Distillation Alternation for Resource-Constrained IoTcited by 2
  16. 2025Adaptive Biased User Scheduling for Heterogeneous Wireless Federate Learning Networkcited by 1
  17. 2025FedBiF: Communication-Efficient Federated Learning via Bits Freezingcited by 1

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