mlsanityData to 14 Oct 2026

Energy-Harvesting Distributed Machine Learning

arXiv 2102.05639 ↗First posted 10 Feb 2021cs.LG cs.IT math.ITcited by 6

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Abstract (arXiv)

This paper provides a first study of utilizing energy harvesting for sustainable machine learning in distributed networks. We consider a distributed learning setup in which a machine learning model is trained over a large number of devices that can harvest energy from the ambient environment, and develop a practical learning framework with theoretical convergence guarantees. We demonstrate through numerical experiments that the proposed framework can significantly outperform energy-agnostic benchmarks. Our framework is scalable, requires only local estimation of the energy statistics, and can be applied to a wide range of distributed training settings, including machine learning in wireless networks, edge computing, and mobile internet of things.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 14 of its 29 references that are in the map. First posted on arXiv 10 Feb 2021.

Builds on

14 of 29 references.

References · 14

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,254
  4. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  5. 2018Local SGD Converges Fast and Communicates Littlecited by 605
  6. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  7. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 198
  8. 2018cpSGD: Communication-efficient and differentially-private distributed SGDcited by 158
  9. 2020Optimal Client Sampling for Federated Learningcited by 124
  10. 2020Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learningcited by 78
  11. 2020Scheduling for Cellular Federated Edge Learning with Importance and Channel Awarenesscited by 62
  12. 2019Energy-Efficient Radio Resource Allocation for Federated Edge Learningcited by 39
2 more
  1. 2019Energy-Aware Analog Aggregation for Federated Learning with Redundant Datacited by 23
  2. 2013Energy Cooperation in Energy Harvesting Communicationscited by 1

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 2.

6 papers.

  1. 2023A Systematic Review of Green AIcited by 77
  2. 2023Green Edge AI: A Contemporary Surveycited by 25
  3. 2022On Federated Learning with Energy Harvesting Clientscited by 4
  4. 2024Federated Learning With Energy Harvesting Devices: An MDP Frameworkcited by 4
  5. 2025Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learningcited by 2
  6. 2025Choosing to Be Green: Advancing Green AI via Dynamic Model Selectioncited by 2