Adaptive federated learning device selection strategy based on edge-end performance prediction

Ran Yu; Donghui Zhang; Kailiang Wang; Sihan Wei; Kunrui Tong; Xiao Liu; Min Liu; Jianwei Ren; Wei Song

doi:10.1117/12.3049477

18 October 2024 Adaptive federated learning device selection strategy based on edge-end performance prediction

Ran Yu, Donghui Zhang, Kailiang Wang, Sihan Wei, Kunrui Tong, Xiao Liu, Min Liu, Jianwei Ren, Wei Song

Proceedings Volume 13277, Sixth International Conference on Wireless Communications and Smart Grid (ICWCSG 2024); 132770W (2024) https://doi.org/10.1117/12.3049477
Event: 2024 6th International Conference on Wireless Communications and Smart Grid, 2024, Sipsongpanna, China

Abstract

Aiming at the problems of performance fluctuation and unstable training of edge-end devices during training, this paper proposes an adaptive federated learning device selection strategy based on edge-end performance prediction. The method aims to solve the problem that the traditional federated device selection strategy does not consider the performance of edge-end devices, which leads to a large amount of wasted arithmetic and communication resources and slow convergence speed. Specifically, the method establishes a linear regression model based on the overhead of each resource by collecting the historical performance data of edge-end devices to realize the performance prediction based on the amount of local data; and realizes an efficient and stable federation training process in dynamic edge-end environments by realizing a device selection decision module based on the dobby slot machine algorithm, which adaptively learns the linkage between the performance characteristics of the devices and the device selection decision. Experimental results show that the method proposed in this paper can select stable devices for updating as much as possible, and has significant advantages in ensuring high global convergence speed and high training accuracy

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ran Yu, Donghui Zhang, Kailiang Wang, Sihan Wei, Kunrui Tong, Xiao Liu, Min Liu, Jianwei Ren, and Wei Song "Adaptive federated learning device selection strategy based on edge-end performance prediction", Proc. SPIE 13277, Sixth International Conference on Wireless Communications and Smart Grid (ICWCSG 2024), 132770W (18 October 2024); https://doi.org/10.1117/12.3049477

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