Federated learning (FL) emerges as a distributed training method in the Internet of Vehicles (IoVs), which promotes connected and automated vehicles (CAVs) to train a global model by exchanging models instead of raw data to protect data privacy. In this paper, consider the limitation of model accuracy and communication overhead in FL, as well as further verification in the real scenarios, we propose a directed acyclic graph (DAG) blockchain-based IoV system that comprises a DAG layer and a CAV layer for model sharing and training, respectively. Furthermore, a DAG blockchain-assisted asynchronous federated mutual learning (DAFML) algorithm is introduced to improve the model accuracy, which utilizes mutual distillation method to train a teacher-student model simultaneously. Moreover, a policy network will first be pre-trained by an expert data augmentation strategy through the DAFML algorithm via the behavior cloning, and be re-trained through the proposed proximal policy optimization (PPO) algorithm based autonomous driving framework. Finally, simulation results demonstrate that the proposed DAFML algorithm outperforms other benchmarks in terms of the model accuracy, distillation ratio and autonomous driving decision.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    DAG Blockchain-Assisted Asynchronous Federated Mutual Learning for Autonomous Driving


    Beteiligte:
    Wu, Yuhang (Autor:in) / Huang, Xiaoge (Autor:in) / Cao, Bin (Autor:in) / Liang, Chengchao (Autor:in) / Chen, Qianbin (Autor:in)


    Erscheinungsdatum :

    01.05.2025


    Format / Umfang :

    12094544 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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

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


    AFLChain: Blockchain-enabled Asynchronous Federated Learning in Edge Computing Network

    Huang, Xiaoge / Deng, Xuesong / Chen, Qianbin et al. | IEEE | 2023


    Deep Federated Learning for Autonomous Driving

    Nguyen, Anh / Do, Tuong / Tran, Minh et al. | IEEE | 2022


    Asynchronous Federated Learning for Edge-assisted Vehicular Networks

    Wang, Siyuan / Wu, Qiong / Fan, Qiang et al. | ArXiv | 2022

    Freier Zugriff

    Privacy-Preserved Federated Learning for Autonomous Driving

    Li, Yijing / Tao, Xiaofeng / Zhang, Xuefei et al. | IEEE | 2022