Wireless Federated Learning over UAV-enabled Integrated Sensing and Communication
This addresses latency issues in federated learning for UAV-enabled systems, which is an incremental improvement in a specific domain.
The paper tackles latency optimization in federated learning over UAV networks with integrated sensing and communication by jointly optimizing UAV trajectories and resource allocation, achieving up to 68.54% latency reduction compared to benchmarks.
This paper studies a new latency optimization problem in unmanned aerial vehicles (UAVs)-enabled federated learning (FL) with integrated sensing and communication. In this setup, distributed UAVs participate in model training using sensed data and collaborate with a base station (BS) serving as FL aggregator to build a global model. The objective is to minimize the FL system latency over UAV networks by jointly optimizing UAVs' trajectory and resource allocation of both UAVs and the BS. The formulated optimization problem is troublesome to solve due to its non-convexity. Hence, we develop a simple yet efficient iterative algorithm to find a high-quality approximate solution, by leveraging block coordinate descent and successive convex approximation techniques. Simulation results demonstrate the effectiveness of our proposed joint optimization strategy under practical parameter settings, saving the system latency up to 68.54\% compared to benchmark schemes.