In-depth analysis of medical data through machine learning to achieve disease prediction is beneficial to the early detection and treatment of diseases. However, medical data involves mass patient privacy, and datasets of different medical institutions cannot be directly shared due to privacy protection. So medical data often exists in the form of data islands, which makes it difficult for most existing prediction models to complete disease prediction. In this paper, a digital twin assisted efficient clustering Federated Learning (FL) algorithm for disease prediction is proposed. It can break data islands to predict diseases on the premise of privacy security. Firstly, we design an efficient clustering Federated Learning with Client Selection (FLCS) protocol based on heterogeneity and contribution to improve the training efficiency and prediction accuracy. Secondly, we use digital twin to assist the FLCS protocol to carry out large-scale prediction. In addition, the shapley value introduced in the calculation of client contribution makes the model interpretable and enhances the reliability of prediction results. Finally, the evaluation results show that compared with the common prediction models and FedAvg algorithm, the FLCS protocol assisted by digital twin has better efficiency and accuracy in binary classification prediction.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:
    Yuan, Xiaoming (Autor:in) / Zhang, Jialin (Autor:in) / Luo, Jingqi (Autor:in) / Chen, Jiahui (Autor:in) / Shi, Zhiguo (Autor:in) / Qin, Mingwei (Autor:in)


    Erscheinungsdatum :

    01.06.2022


    Format / Umfang :

    926808 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Digital twin traffic flow prediction system based on graph federated learning

    XING LING / GAO JIANPING / LI BING et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    Robust Clustered Federated Learning Against Malicious Agents

    Ngoh, Sisui / Majumder, Abhishek Pal / Duan, Lingjie | IEEE | 2024


    Clustered Vehicular Federated Learning: Process and Optimization

    Taik, Afaf / Mlika, Zoubeir / Cherkaoui, Soumaya | IEEE | 2022



    Federated Generative Learning for Digital Twin Network Modeling

    Jin, Dongzi / Li, Yingyu / Xiao, Yong | IEEE | 2024