Federated Linear Bandit Learning Via UAV Aided Over-the-Air Computation

This paper investigates federated contextual linear bandit learning in a wireless network with a central server and multiple devices. To reduce communication latency, devices interact with the server via over-the-air computation (AirComp) over noisy, fading channels, where signal distortion can occur due to channel imperfections. Departing from traditional AirComp designs for static networks, we propose a novel federated bandit learning framework that leverages unmanned aerial vehicles (UAVs) as mobile servers to aggregate data from distributed IoT devices. To optimize this system, we employ a block coordinate descent method combined with the alternating direction method of multipliers (BCD-ADMM), jointly optimizing the UAV trajectory, receive normalization factor, and transmission power to minimize the time-averaged mean square error (MSE) of AirComp. Our approach addresses the challenge of decentralized data across multiple devices, enabling secure and efficient collaboration without direct data sharing. Theoretical analysis establishes an upper bound on the algorithm's regret, affirming the framework's scalability and robustness against noise. Simulation results support these findings, highlighting notable performance improvements in federated bandit learning with UAV-assisted AirComp.