What question did this study set out to answer?

The aim is to enhance clinical event prediction across distributed hospitals while preserving privacy and addressing catastrophic forgetting during global updates.

March 10, 2026Scientific Reports0 citationsOpen Access

Federated learning with continual update for privacy-preserving clinical event prediction across distributed hospitals using MCN-GNN

KJK. Jagdeesh NKN. Kanimozhi TSTanvir H. Sardar

Key Points

The aim is to enhance clinical event prediction across distributed hospitals while preserving privacy and addressing catastrophic forgetting during global updates.
Utilized federated learning to securely train models across hospitals.
Implemented Meta Experience Polynomial Decay-based Replay for continual learning.
Constructed Temporal-Causal Graph for data modeling and feature extraction.
Employed Calinski–Harabasz Index for data aggregation in global updates.
Stored transaction data on blockchain for enhanced traceability.
Achieved clinical event prediction accuracy of 98.97%.
Outperformed existing predictive models through the proposed federated learning methodology.
Successfully maintained model integrity and privacy across hospitals.

Abstract

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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