Federated Learning via Unmanned Aerial Vehicle

Federated learning (FL) has emerged as a promising alternative to centralized machine learning for exploiting large amounts of data generated by networks while ensuring data privacy. Unlike previous FL works that rely on terrestrial base stations, this paper studies an unmanned aerial vehicle (UAV)-assisted FL system where a UAV collects local models from distributed ground devices. By leveraging the UAV's high altitude and mobility, it can proactively establish short-distance line-of-sight links with devices to mitigate the communication straggler effect and improve communication efficiency in FL. Specifically, we present the convergence analysis of FL without convexity assumptions, demonstrating the effect of device scheduling on the global gradients. Based on the derived convergence bound, we aim to minimize the completion time of FL training 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 closed-form solutions via the Lagrange dual ascent method. Simulation results show that the proposed design significantly improves the tradeoff between completion time and test accuracy compared to existing benchmarks.