  Implementing federated learning (FL) algorithms in wireless networks has
garnered a wide range of attention. However, few works have considered the
impact of user mobility on the learning performance. To fill this research gap,
firstly, we develop a theoretical model to characterize the hierarchical
federated learning (HFL) algorithm in wireless networks where the mobile users
may roam across multiple edge access points, leading to incompletion of
inconsistent FL training. Secondly, we provide the convergence analysis of HFL
with user mobility. Our analysis proves that the learning performance of HFL
deteriorates drastically with highly-mobile users. And this decline in the
learning performance will be exacerbated with small number of participants and
large data distribution divergences among local data of users. To circumvent
these issues, we propose a mobility-aware cluster federated learning (MACFL)
algorithm by redesigning the access mechanism, local update rule and model
aggregation scheme. Finally, we provide experiments to evaluate the learning
performance of HFL and our MACFL. The results show that our MACFL can enhance
the learning performance, especially for three different cases, namely, the
case of users with non-independent and identical distribution data, the case of
users with high mobility, and the cases with a small number of users.
