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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月8日

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  • K. Jagdeesh,
  • N. Kanimozhi,
  • Tanvir H. Sardar,
  • N. Naveenkumar,
  • B. Mahalakshmi,
  • A. Chandrasekar,
  • M. Karpagam,
  • Sk Mahmudul Hasan

摘要

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.