  With huge amounts of data explosively increasing in the mobile edge,
over-the-air federated learning (OA-FL) emerges as a promising technique to
reduce communication costs and privacy leak risks. However, when devices in a
relatively large area cooperatively train a machine learning model, the
attendant straggler issues will significantly reduce the learning performance.
In this paper, we propose an unmanned aerial vehicle (UAV) assisted OA-FL
system, where the UAV acts as a parameter server (PS) to aggregate the local
gradients hierarchically for global model updating. Under this UAV-assisted
hierarchical aggregation scheme, we carry out a gradient-correlation-aware FL
performance analysis. We then formulate a mean squared error (MSE) minimization
problem to tune the UAV trajectory and the global aggregation coefficients
based on the analysis results. An algorithm based on alternating optimization
(AO) and successive convex approximation (SCA) is developed to solve the
formulated problem. Simulation results demonstrate the great potential of our
UAV-assisted hierarchical aggregation scheme.
