mlsanityData to 7 Oct 2026

Sustainable Federated Learning

arXiv 2102.11274 ↗First posted 22 Feb 2021cs.LG cs.IT math.ITcited by 5

Not on the map. Why?

Abstract (arXiv)

Potential environmental impact of machine learning by large-scale wireless networks is a major challenge for the sustainability of future smart ecosystems. In this paper, we introduce sustainable machine learning in federated learning settings, using rechargeable devices that can collect energy from the ambient environment. We propose a practical federated learning framework that leverages intermittent energy arrivals for training, with provable convergence guarantees. Our framework can be applied to a wide range of machine learning settings in networked environments, including distributed and federated learning in wireless and edge networks. Our experiments demonstrate that the proposed framework can provide significant performance improvement over the benchmark energy-agnostic federated learning settings.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 26 of its 37 references that are in the map. First posted on arXiv 22 Feb 2021.

Builds on

26 of 37 references.

References · 26

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  5. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  6. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,242
  7. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  8. 2019Towards Federated Learning at Scale: System Designcited by 826
  9. 2019Green AIcited by 620
  10. 2018Local SGD Converges Fast and Communicates Littlecited by 595
  11. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
  12. 2019Federated Machine Learning: Concept and Applicationscited by 409
14 more
  1. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  2. 2019Fair Resource Allocation in Federated Learningcited by 389
  3. 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
  4. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 202
  5. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 196
  6. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
  7. 2018cpSGD: Communication-efficient and differentially-private distributed SGDcited by 157
  8. 2020Optimal Client Sampling for Federated Learningcited by 123
  9. 2020Privacy-preserving Traffic Flow Prediction: A Federated Learning Approachcited by 82
  10. 2019Active Federated Learningcited by 63
  11. 2019Energy Demand Prediction with Federated Learning for Electric Vehicle Networkscited by 46
  12. 2019Energy-Efficient Radio Resource Allocation for Federated Edge Learningcited by 39
  13. 2019Federated Learning with Autotuned Communication-Efficient Secure Aggregationcited by 23
  14. 2013Movers and Shakers: Kinetic Energy Harvesting for the Internet of Thingscited by 3

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 2; 2023: 0; 2024: 0; 2025: 1.

5 papers.

  1. 2021SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learningcited by 15
  2. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
  3. 2022On Federated Learning with Energy Harvesting Clientscited by 4
  4. 2022Regulating Gatekeeper AI and Data: Transparency, Access, and Fairness under the DMA, the GDPR, and beyondcited by 4
  5. 2025Energy-Aware Deep Learning on Resource-Constrained Hardwarecited by 3