Federated Explainable Deep Learning in Healthcare

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This document explores smart healthcare and clinical outcome prediction, highlighting the privacy risks of centralized data sharing. It introduces federated learning and explainable AI as solutions. The FED-XDL framework is detailed, covering local data preparation, CNN-BiLSTM-Transformer learning, and privacy protection. Evaluation focuses on comparing accuracy, privacy, and costs, while presenting SHAP findings for feature importance. The discussion includes risk levels, recommendations,...