With the increasing demand for application development of task publishers (e.g., automobile enterprises) in the Internet of Vehicles (IoV), federated learning (FL) can be used to enable vehicle users (VUs) to conduct local application training without disclosing data. However, the challenges of VUs’ intermittent connectivity, low proactivity, and limited resources are inevitable issues in the process of FL. In this paper, we propose a UAV-assisted FL framework in the context of the IoV. An incentive stage and a training stage are involved in this framework. UAVs serve as central servers, which assist to incentivize VUs, manage VUs’ contributed resources, and provide model aggregation, making sure communication efficiency and mobility enhancement in FL. The numerical results show that, compared with the baseline algorithms, the proposed algorithm reduces energy consumption by 50.3% and improves model convergence speed by 30.6%.


    Access

    Download


    Export, share and cite



    Title :

    Joint Incentive Mechanism Design and Energy-Efficient Resource Allocation for Federated Learning in UAV-Assisted Internet of Vehicles


    Contributors:
    Shangjing Lin (author) / Yueying Li (author) / Zhibo Han (author) / Bei Zhuang (author) / Ji Ma (author) / Huaglory Tianfield (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Incentive Mechanism Design for Federated Learning in the Internet of Vehicles

    Lim, Wei Yang Bryan / Xiong, Zehui / Niyato, Dusit et al. | IEEE | 2020


    FedBeam: Reliable Incentive Mechanisms for Federated Learning in UAV-Enabled Internet of Vehicles

    Gangqiang Hu / Donglin Zhu / Jiaying Shen et al. | DOAJ | 2024

    Free access

    Incentive Mechanism Design in Semi-Asynchronous Blockchain-based Federated Learning

    Liu, Xuanzhang / Liu, Jiyao / Wei, Xinliang et al. | IEEE | 2024


    ADMM-based Energy-Efficient Resource Allocation Method for Internet of Vehicles

    Han, Shuangshuang / Chen, Yueyun / Du, Liping et al. | IEEE | 2022


    Joint Partial Offloading and Resource Allocation for Vehicular Federated Learning Tasks

    Ma, Guifu / Hu, Manjiang / Wang, Xiaowei et al. | IEEE | 2024