mlsanityData to 14 Oct 2026

Towards Energy-Aware Federated Learning on Battery-Powered Clients

arXiv 2208.04505 ↗First posted 9 Aug 2022cs.LG cs.DCcited by 11

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Abstract (arXiv)

Federated learning (FL) is a newly emerged branch of AI that facilitates edge devices to collaboratively train a global machine learning model without centralizing data and with privacy by default. However, despite the remarkable advancement, this paradigm comes with various challenges. Specifically, in large-scale deployments, client heterogeneity is the norm which impacts training quality such as accuracy, fairness, and time. Moreover, energy consumption across these battery-constrained devices is largely unexplored and a limitation for wide-adoption of FL. To address this issue, we develop EAFL, an energy-aware FL selection method that considers energy consumption to maximize the participation of heterogeneous target devices. EAFL is a power-aware training algorithm that cherry-picks clients with higher battery levels in conjunction with its ability to maximize the system efficiency. Our design jointly minimizes the time-to-accuracy and maximizes the remaining on-device battery levels. EAFLimproves the testing model accuracy by up to 85% and decreases the drop-out of clients by up to 2.45×.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 28 of its 50 references that are in the map. First posted on arXiv 9 Aug 2022.

Builds on

28 of 50 references.

References · 28

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16 more
  1. 2019Asynchronous Federated Optimizationcited by 267
  2. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 204
  3. 2019Federated Adversarial Domain Adaptationcited by 140
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  8. 2020Towards Flexible Device Participation in Federated Learningcited by 65
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  10. 2020Can Federated Learning Save The Planet?cited by 52
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Cited by year

Citing papers per year: 2022: 1; 2023: 3; 2024: 4; 2025: 3.

11 papers.

  1. 2023FedZero: Leveraging Renewable Excess Energy in Federated Learningcited by 11
  2. 2022CC-FedAvg: Computationally Customized Federated Averagingcited by 2
  3. 2023Leveraging The Edge-to-Cloud Continuum for Scalable Machine Learning on Decentralized Datacited by 1
  4. 2023An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edgecited by 1
  5. 2024Energy-Aware Decentralized Learning with Intermittent Model Trainingcited by 1
  6. 2024SCALE: Self-regulated Clustered federAted LEarning in a Homogeneous Environmentcited by 1
  7. 2025Adaptive Client Selection via Q-Learning-based Whittle Index in Wireless Federated Learningcited by 1
  8. 2024Federated Computing -- Survey on Building Blocks, Extensions and Systemscited by 0
  9. 2024Learn More by Using Less: Distributed Learning with Energy-Constrained Devicescited by 0
  10. 2025Semi-decentralized Federated Time Series Prediction with Client Availability Budgetscited by 0
  11. 2025Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregationcited by 0