  Unmanned aerial vehicles (UAVs) mobility enables flexible and customized
federated learning (FL) at the network edge. However, the underlying
uncertainties in the aerial-terrestrial wireless channel may lead to a biased
FL model. In particular, the distribution of the global model and the
aggregation of the local updates within the FL learning rounds at the UAVs are
governed by the reliability of the wireless channel. This creates an
undesirable bias towards the training data of ground devices with better
channel conditions, and vice versa. This paper characterizes the global bias
problem of aerial FL in large-scale UAV networks. To this end, the paper
proposes a channel-aware distribution and aggregation scheme to enforce equal
contribution from all devices in the FL training as a means to resolve the
global bias problem. We demonstrate the convergence of the proposed method by
experimenting with the MNIST dataset and show its superiority compared to
existing methods. The obtained results enable system parameter tuning to
relieve the impact of the aerial channel deficiency on the FL convergence rate.
