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REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning over Mobile Devices

arXiv 2309.13643 ↗First posted 24 Sep 2023cs.LG cs.NIcited by 0

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Abstract (arXiv)

Participant selection (PS) helps to accelerate federated learning (FL) convergence, which is essential for the practical deployment of FL over mobile devices. While most existing PS approaches focus on improving training accuracy and efficiency rather than residual energy of mobile devices, which fundamentally determines whether the selected devices can participate. Meanwhile, the impacts of mobile devices' heterogeneous wireless transmission rates on PS and FL training efficiency are largely ignored. Moreover, PS causes the staleness issue. Prior research exploits isolated functions to force long-neglected devices to participate, which is decoupled from original PS designs. In this paper, we propose a residual energy and wireless aware PS design for efficient FL training over mobile devices (REWAFL). REW AFL introduces a novel PS utility function that jointly considers global FL training utilities and local energy utility, which integrates energy consumption and residual battery energy of candidate mobile devices. Under the proposed PS utility function framework, REW AFL further presents a residual energy and wireless aware local computing policy. Besides, REWAFL buries the staleness solution into its utility function and local computing policy. The experimental results show that REW AFL is effective in improving training accuracy and efficiency, while avoiding "flat battery" of mobile devices.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 60 references that are in the map. First posted on arXiv 24 Sep 2023.

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32 of 60 references.

References · 32

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20 more
  1. 2019Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronizationcited by 107
  2. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 103
  3. 2018LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learningcited by 96
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  13. 2023Federated Learning-Empowered AI-Generated Content in Wireless Networkscited by 26
  14. 2019Federated Learning for Ranking Browser History Suggestionscited by 24
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  16. 2021Fast Federated Learning by Balancing Communication Trade-Offscited by 18
  17. 2023Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshnesscited by 15
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  19. 2022Energy-Aware Edge Association for Cluster-based Personalized Federated Learningcited by 6
  20. 2017Coordinate Descent with Bandit Samplingcited by 5

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