Considering the limited resources, user mobility and unpredictable driving environment in intelligent driving, this paper studies the optimal training efficiency of federated learning for distributed training of collision warning services with the assistance of digital twin (DT). DT is emerging as one of the most promising technologies to make the digital representation of physical components for better prediction, analysis, and optimization of various services in intelligent driving. we first propose a DT-enabled collision warning framework, including physical network layer, digital twin layer, and application layer. Then, for the cooperative training of multi-level warning models combining gate recurrent unit (GRU) and support vector machine (SVM) in the digital twin layer, we propose semi-asynchronous federated learning with adaptive adjustment of parameters (SFLAAP) scheme. We aim at minimizing the training delay of collision warning model by dynamically adjusting the training parameters according to real-time training state and resource conditions of digital space, specifically the local training times and the number of local nodes participating in the aggregation, while ensuring the accuracy of the model. Considering the complexity of the target problem, we propose parameter adjustment algorithm based on asynchronous advantage actor-critic (A3C). Experiments on the classical dataset show high effectiveness of the proposed algorithms. Specifically, SFLAAP can reduce the completion time by about 12% and improve the learning accuracy by about 1%, compared with the state-of-the-art solutions.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Digital Twin-Enabled Efficient Federated Learning for Collision Warning in Intelligent Driving


    Beteiligte:
    Tang, Lun (Autor:in) / Wen, Mingyan (Autor:in) / Shan, Zhenzhen (Autor:in) / Li, Li (Autor:in) / Liu, Qinghai (Autor:in) / Chen, Qianbin (Autor:in)


    Erscheinungsdatum :

    01.03.2024


    Format / Umfang :

    11052799 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Digital Twin Enabled Multi-task Federated Learning in Heterogeneous Vehicular Networks

    Hui, Yilong / Zhao, Gaosheng / Yin, Zhisheng et al. | IEEE | 2022



    Collision early warning method based on adaptive federated learning

    TANG LUN / WEN MINGYAN / SHAN ZHENZHEN et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Intelligent collision avoidance and safety warning system for car driving

    Nyamati, Vikas / Chaudhuri, Tridha / Jayavel, Kayalvizhi | IEEE | 2017


    An Efficient Digital Twin Assisted Clustered Federated Learning Algorithm for Disease Prediction

    Yuan, Xiaoming / Zhang, Jialin / Luo, Jingqi et al. | IEEE | 2022