  To enable communication-efficient federated learning (FL), this paper studies
an unmanned aerial vehicle (UAV)-enabled FL system, where the UAV coordinates
distributed ground devices for a shared model training. Specifically, by
exploiting the UAV's high altitude and mobility, the UAV can proactively
establish short-distance line-of-sight links with devices and prevent any
device from being a communication straggler. Thus, the model aggregation
process can be accelerated while the cumulative model loss caused by device
scheduling can be reduced, resulting in a decreased completion time. We first
present the convergence analysis of FL without the assumption of convexity,
demonstrating the effect of device scheduling on the global gradients. Based on
the derived convergence bound, we further formulate the completion time
minimization problem by jointly optimizing device scheduling, UAV trajectory,
and time allocation. This problem explicitly incorporates the devices' energy
budgets, dynamic channel conditions, and convergence accuracy of FL
constraints. Despite the non-convexity of the formulated problem, we exploit
its structure to decompose it into two sub-problems and further derive the
optimal solutions via the Lagrange dual ascent method. Simulation results show
that the proposed design significantly improves the tradeoff between completion
time and prediction accuracy in practical FL settings compared to existing
benchmarks.
