In this paper, we propose a solution for secure and communication-efficient Vertical Federated Learning (VFL) applied to charging station recommendations in Intelligent Connected Vehicles (ICVs). Our methodology leverages a hybrid security strategy combining threshold partially homomorphic encryption (PHE) and additive secret sharing scheme (SSS) to ensure data privacy and security during model training. The threshold PHE enables encrypted operations, allowing a subset of participants to decrypt results, while SSS further protects gradients by splitting them into multiple shares across participants. To enhance communication efficiency, we employ asynchronous updates and gradient compression techniques, reducing synchronization and communication overhead. The approach is evaluated using three datasets and four baseline models, with results demonstrating strong recommendation accuracy, security, and reduced communication costs. This solution provides a secure and efficient VFL framework for ICV charging station recommendations.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Secure Vertical Federated Learning for Charging Station Recommendation in Connected Vehicles


    Contributors:
    Zhang, Xiaobo (author) / Chen, Tao (author) / Liang, Haolin (author) / Zeng, Jing (author) / Wu, Yehui (author) / Zhang, Fan (author)


    Publication date :

    2024-10-23


    Size :

    501064 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Federated Learning for Drowsiness Detection in Connected Vehicles

    Lindskog, William / Spannagl, Valentin / Prehofer, Christian | Springer Verlag | 2023


    Federated learning for connected camera applications in vehicles

    ZHANG WENDE / ANDERSSON ERIK / GAYLORD MICHAEL THOMAS et al. | European Patent Office | 2023

    Free access


    BlockEV: Efficient and Secure Charging Station Selection for Electric Vehicles

    Danish, Syed Muhammad / Zhang, Kaiwen / Jacobsen, Hans-Arno et al. | IEEE | 2021