Mobility accelerates learning: convergence analysis on hierarchical federated learning in vehicular networks
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
Hierarchical federated learning (HFL) enables distributed training of models across multiple devices with the help of several edge servers and a cloud edge server in a privacy-preserving manner. In this paper, we consider HFL with highly mobile devices, mainly targeting at vehicular networks. Through convergence analysis, we show that mobility influences the convergence speed by both fusing the edge data and shuffling the edge models. While mobility is usually considered as a challenge from the perspective of communication, we prove that it increases the convergence speed of HFL with edge-level heterogeneous data, since more diverse data can be incorporated. Furthermore, we demonstrate that a higher speed leads to faster convergence, since it accelerates the fusion of data. Simulation results show that mobility increases the model accuracy of HFL by up to 15.1% when training a convolutional neural network on the CIFAR-10 dataset.
Date Issued
2025-01-01
Date Acceptance
2024-08-30
Citation
IEEE Transactions on Vehicular Technology, 2025, 74 (1), pp.1657-1673
ISSN
0018-9545
Publisher
Institute of Electrical and Electronics Engineers
Start Page
1657
End Page
1673
Journal / Book Title
IEEE Transactions on Vehicular Technology
Volume
74
Issue
1
Copyright Statement
Copyright © 2025 IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Publication Status
Published
Date Publish Online
2025-01-15