This chapter focuses on an unmanned aerial vehicle (UAV) assisted FEEL system, where the UAV is in charge of coordinating distributed model training among ground devices. The UAV’s high altitude and mobility are leveraged to establish short-distance line-of-sight links with devices, preventing any single device from becoming a communication bottleneck. This approach accelerates model aggregation and reduces cumulative model loss due to device scheduling, thereby decreasing the overall completion time. We illustrate the impact of device scheduling on the convergence of the FEEL system and formulate a training time minimization problem with respect to device scheduling and UAV trajectory, followed by developing an alternating Lagrange dual ascent algorithm.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Federated Edge Learning via Unmanned Aerial Vehicle


    Weitere Titelangaben:

    Wireless Networks


    Beteiligte:
    Zhou, Yong (Autor:in) / Fang, Wenzhi (Autor:in) / Shi, Yuanming (Autor:in) / Letaief, Khaled B. (Autor:in)

    Erschienen in:

    Federated Edge Learning ; Kapitel : 6 ; 101-122


    Erscheinungsdatum :

    09.06.2025


    Format / Umfang :

    22 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Experimental Results of Federated Learning in Unmanned Aerial Vehicle Swarm

    Shen, Yun / Dong, Chao / Qu, Yuben et al. | IEEE | 2021


    Unmanned aerial vehicle stealth leading edge and unmanned aerial vehicle

    WEI NANNAN / YANG XIAOYA / LI XUAN et al. | Europäisches Patentamt | 2023

    Freier Zugriff



    Unmanned aerial vehicle and unmanned aerial vehicle system

    WANG JIEKAI / ZHOU GUANGHAI / LIU SHAOYI et al. | Europäisches Patentamt | 2023

    Freier Zugriff