  Federated learning (FL) over wireless communication channels, specifically,
over-the-air (OTA) model aggregation framework is considered. In OTA wireless
setups, the adverse channel effects can be alleviated by increasing the number
of receive antennas at the parameter server (PS), which performs model
aggregation. However, the performance of OTA FL is limited by the presence of
mobile users (MUs) located far away from the PS. In this paper, to mitigate
this limitation, we propose hierarchical over-the-air federated learning
(HOTAFL), which utilizes intermediary servers (IS) to form clusters near MUs.
We provide a convergence analysis for the proposed setup, and demonstrate
through theoretical and experimental results that local aggregation in each
cluster before global aggregation leads to a better performance and faster
convergence than OTA FL.
