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

Abstract

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.

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