Wireless Federated Learning over UAV-enabled Integrated Sensing and Communication

TL;DR

This paper proposes a joint optimization approach for UAV-enabled federated learning systems with integrated sensing and communication, significantly reducing system latency through trajectory and resource allocation optimization.

Contribution

It introduces a novel latency minimization framework for UAV-based federated learning with a new iterative algorithm to optimize UAV trajectories and resource allocation.

Findings

Achieves up to 68.54% latency reduction compared to benchmarks.

Develops an efficient iterative algorithm using block coordinate descent and convex approximation.

Demonstrates effectiveness through simulation under practical parameters.

Abstract

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.