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.

翻译：层次化联邦学习（HFL）通过多个边缘服务器与云端服务器协同，以隐私保护方式实现跨设备分布式模型训练。本文面向高移动性设备场景，重点针对车载网络开展研究。通过收敛性分析，我们揭示了移动性通过融合边缘数据与重组边缘模型双重机制影响收敛速度。尽管从通信视角看，移动性通常被视为挑战，但本文证明：在边缘数据异质性条件下，移动性能通过引入更多元化的数据提升HFL的收敛速度。进一步研究表明，更高的移动速度能加速数据融合进程，从而带来更快的收敛性能。仿真结果表明，在CIFAR-10数据集上训练卷积神经网络时，移动性可使HFL模型准确率提升高达15.1%。