Predicting traffic flow plays an important role in reducing traffic congestion and improving transportation efficiency for smart cities. Traffic Flow Prediction (TFP) in the smart city requires efficient models, highly reliable networks, and data privacy. As traffic data, traffic trajectory can be transformed into a graph representation, so as to mine the spatio-temporal information of the graph for TFP. However, most existing work adopt a central training mode where the privacy problem brought by the distributed traffic data is not considered. In this paper, we propose a Federated Deep Learning based on the Spatial-Temporal Long and Short-Term Networks (FedSTN) algorithm to predict traffic flow by utilizing observed historical traffic data. In FedSTN, each local TFP model deployed in an edge computing server includes three main components, namely Recurrent Long-term Capture Network (RLCN) module, Attentive Mechanism Federated Network (AMFN) module, and Semantic Capture Network (SCN) module. RLCN can capture the long-term spatial-temporal information in each area. AMFN shares short-term spatio-temporal hidden information when it trains its local TFP model by the additive homomorphic encryption approach based on Vertical Federated Learning (VFL). We employ SCN to capture semantic features such as irregular non-Euclidean connections and Point of Interest (POI). Compared with existing baselines, several simulations are conducted on practical data sets and the results prove the effectiveness of our algorithm.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    FedSTN: Graph Representation Driven Federated Learning for Edge Computing Enabled Urban Traffic Flow Prediction


    Contributors:
    Yuan, Xiaoming (author) / Chen, Jiahui (author) / Yang, Jiayu (author) / Zhang, Ning (author) / Yang, Tingting (author) / Han, Tao (author) / Taherkordi, Amir (author)


    Publication date :

    2023-08-01


    Size :

    2244982 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Cluster type federated learning-based urban traffic flow prediction method

    ZHANG ZIJIAN / BAI SIMENG / WU YAN et al. | European Patent Office | 2024

    Free access

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

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


    Digital twin traffic flow prediction system based on graph federated learning

    XING LING / GAO JIANPING / LI BING et al. | European Patent Office | 2024

    Free access

    Traffic flow prediction method based on federated learning and graph neural network

    MI BO / ZENG RAN / HUANG DARONG et al. | European Patent Office | 2024

    Free access