In the foreseeable Intelligent Transportation System, Intelligent Connected Vehicles (ICVs) will play an important role in improving travel efficiency and safety. However, it is challenging for ICVs to support the resource-hungry autonomous driving applications due to the limitation of hardware computing power. Fortunately, the emergence of Multi-access Edge Computing helps overcome this limitation effectively. This paper addresses the vehicle-to-edge server computation offloading conundrum by optimizing the trade-offs in partial offloading and resource allocation. Proposing a distributed approach, this study confronts the multi-variable non-convex challenge directly by decoupling variables and deriving constraint-based bounds that guide the decisions for offloading and allocation. A novel low-complexity distributed algorithm is introduced that not only tends toward optimal but also demonstrates superior real-time applicability and efficiency, illustrated through enhanced performances both in simulated trials and genuine vehicular edge computing settings. The algorithm’s practical effectiveness addresses a notable gap between the theoretical models for computation offloading and actual real-life execution, reinforcing the soundness and relevance of the proposed method. Furthermore, its advanced integration with federated learning frameworks marks a leading-edge application, substantiating significant enhancements in computational efficiency and robustness.


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    Title :

    Joint Partial Offloading and Resource Allocation for Vehicular Federated Learning Tasks


    Contributors:
    Ma, Guifu (author) / Hu, Manjiang (author) / Wang, Xiaowei (author) / Li, Haoran (author) / Bian, Yougang (author) / Zhu, Konglin (author) / Wu, Di (author)


    Publication date :

    2024-08-01


    Size :

    2312357 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English