  Federated learning enables distributed training of machine learning (ML)
models across multiple devices in a privacy-preserving manner. Hierarchical
federated learning (HFL) is further proposed to meet the requirements of both
latency and coverage. In this paper, we consider a data-heterogeneous HFL
scenario with mobility, mainly targeting vehicular networks. We derive the
convergence upper bound of HFL with respect to mobility and data heterogeneity,
and analyze how mobility impacts the performance of HFL. While mobility is
considered as a challenge from a communication point of view, our goal here is
to exploit mobility to improve the learning performance by mitigating data
heterogeneity. Simulation results verify the analysis and show that mobility
can indeed improve the model accuracy by up to 15.1\% when training a
convolutional neural network on the CIFAR-10 dataset using HFL.
