In the Internet of Vehicles (IoV) paradigm, a model owner is able to leverage on the enhanced capabilities of Intelligent Connected Vehicles (ICV) to develop promising Artificial Intelligence (AI) based applications, e.g., for traffic efficiency. However, in some cases, a model owner may have insufficient data samples to build an effective AI model. To this end, we propose a Federated Learning (FL) based privacy preserving approach to facilitate collaborative FL among multiple model owners in the IoV. Our system model enables collaborative model training without compromising data privacy given that only the model parameters instead of the raw data are exchanged within the federation. However, there are two main challenges of incentive mismatches between workers and model owners, as well as among model owners. For the former, we leverage on the self-revealing mechanism in contract theory under information asymmetry. For the latter, we use the coalitional game theory approach that rewards model owners based on their marginal contributions. The numerical results validate the performance efficiency of our proposed hierarchical incentive mechanism design.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Incentive Mechanism Design for Federated Learning in the Internet of Vehicles


    Beteiligte:
    Lim, Wei Yang Bryan (Autor:in) / Xiong, Zehui (Autor:in) / Niyato, Dusit (Autor:in) / Huang, Jianqiang (Autor:in) / Hua, Xian-Sheng (Autor:in) / Miao, Chunyan (Autor:in)


    Erscheinungsdatum :

    01.11.2020


    Format / Umfang :

    1000448 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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

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

    Freier Zugriff

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

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


    Contract Theory Based Incentive Mechanism for Clustered Vehicular Federated Learning

    Wang, Siyang / Zhao, Haitao / Wen, Wanli et al. | IEEE | 2024