Abstract
Federated Learning (FL) enables accurate and secure Clinical Event Prediction (CEP) across distributed hospitals. However, the prevailing works overlooked the catastrophic forgetting during the global update. Therefore, a Meta Experience Polynomial Decay-based Replay (MEPDR)-centric continual update is proposed. Initially, the hospitals (local model) register and log into the blockchain. Then, to train the CEP model, data collection, pre-processing, and feature extraction are performed. Further, the Temporal-Causal Graph (TCG) is constructed. Afterward, the node matrix is created, and the CEP is done using Mean-Centering Normalization-based Graph Neural Network (MCN-GNN). The model’s gradients are further preserved using the Homomorphic Robust Log Scaling-based Encryption (HRLSE). Next, the hospitals are authenticated using the Exponential Probing Digital Signature Algorithm (ExPrDSA). Thereafter, in the global model, the aggregation is performed using the Calinski–Harabasz Index with Zhonghua Distance-based K-Means Clustering (CHIZD-KMC), followed by global CEP. After that, during the global update, the MEPDR-based continual learning is carried out in each local model. Also, the transactions are stored in the blockchain to enhance traceability. Thus, the proposed system effectively predicted the clinical events with an accuracy of 98.97%, outperforming existing works.
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Data availability
The datasets generated during and/or analyzed during the current study are available from publicly available sources.
Code availability
The code developed for this study is provided as Supplementary Material along with this manuscript. It includes the implementations of the proposed models and algorithms used in the experiments. The shared code corresponds to the version used to generate the reported results and is sufficient for reproducing the study. The code is available for academic and research use.
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Jagdeesh, K., Kanimozhi, N., Sardar, T.H. et al. Federated learning with continual update for privacy-preserving clinical event prediction across distributed hospitals using MCN-GNN. Sci Rep (2026). https://doi.org/10.1038/s41598-026-40964-y
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DOI: https://doi.org/10.1038/s41598-026-40964-y
