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Hierarchical federated learning (HFL) enables distributed training of modelsacross multiple devices with the help of several edge servers and a cloud edgeserver in a privacy-preserving manner. In this paper, we consider HFL withhighly mobile devices, mainly targeting at vehicular networks. Throughconvergence analysis, we show that mobility influences the convergence speed byboth fusing the edge data and shuffling the edge models. While mobility isusually considered as a challenge from the perspective of communication, weprove that it increases the convergence speed of HFL with edge-levelheterogeneous data, since more diverse data can be incorporated. Furthermore,we demonstrate that a higher speed leads to faster convergence, since itaccelerates the fusion of data. Simulation results show that mobility increasesthe model accuracy of HFL by up to 15.1% when training a convolutional neuralnetwork on the CIFAR-10 dataset.

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