e-space
Manchester Metropolitan University's Research Repository

    Wireless Federated Learning over UAV-enabled Integrated Sensing and Communication

    Shaon, Shaba, Nguyen, Tien, Mohjazi, Lina, Kaushik, Aryan ORCID logoORCID: https://orcid.org/0000-0001-6252-4641 and Nguyen, Dinh C (2024) Wireless Federated Learning over UAV-enabled Integrated Sensing and Communication. In: 2024 IEEE Conference on Standards for Communications and Networking (CSCN), pp. 365-370. Presented at 2024 IEEE Conference on Standards for Communications and Networking (CSCN), 25 November 2024 - 27 November 2024, Belgrade, Serbia.

    [img]
    Preview
    Accepted Version
    Available under License In Copyright.

    Download (845kB) | Preview

    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.

    Impact and Reach

    Statistics

    Activity Overview
    6 month trend
    10Downloads
    6 month trend
    15Hits

    Additional statistics for this dataset are available via IRStats2.

    Altmetric

    Repository staff only

    Edit record Edit record