Unmanned Aerial Vehicles (UAVs) play a vital role in modern Internet of Things (IoT) ecosystems by providing services like task offloading. Although simultaneous execution can be done through resource optimization and offloading, it is crucial to choose which task to be executed where considering a trade-off between energy consumption and execution delay. Considering these constraints, in this work, we propose a Federated Learning (FL) aided framework for multi-device task offloading in UAV-enabled edge networks while reducing energy consumption and task execution delay. The proposed approach involves a two-step process to execute tasks on various computing devices. In the first step, a task's priority is determined, considering factors like delay deadlines (maximum allowed delay) and resource requirements which include memory, storage and CPU instructions. In the second step, we leverage FL to dynamically calculate energy and delay of the task for different paradigms. This approach aims to maintain a balance between minimizing energy consumption by the UAV and reducing task execution delay. The effectiveness of the proposed framework is evaluated through experiments in terms of energy efficiency and end-to-end execution delay of the UAVs.


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    Titel :

    Efficient Task Offloading Through Federated Learning in UAV-Assisted Edge Networks


    Beteiligte:


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    2039795 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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