Mobility Accelerates Learning: Convergence Analysis on Hierarchical
  Federated Learning in Vehicular Networks
Tan Chen Jintao Yan Yuxuan Sun Sheng Zhou Deniz Gündüz Zhisheng Niu
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.