Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks

• URL: http://arxiv.org/abs/2401.09656v1
• Date: Thu, 18 Jan 2024 00:09:54 GMT
• Title: Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks
• Authors: Tan Chen, Jintao Yan, Yuxuan Sun, Sheng Zhou, Deniz G\"und\"uz, Zhisheng Niu
• Abstract summary: We show that mobility influences the convergence speed by both fusing the edge data and shuffling the edge models. Mobility increases the model accuracy of HFL by up to 15.1% when training a convolutional neural network.
• Score: 15.282996586821415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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