Health-FedNet: secure federated learning for chronic disease prediction on MIMIC-III with differential privacy and homomorphic encryption

The growing trend of utilizing artificial intelligence in healthcare has put it under significant consideration as a means of enhancing early diagnosis and clinical decision-making, though the centralized storage of patient information persists in posing significant privacy, regulatory, and interoperability issues. This paper presents a new federated learning framework named Health-FedNet, which is a privacy-protective and secure predictive model of chronic diseases and is designed to allow training a model using multiple institutions without the transfer of raw clinical information. This framework integrates three main elements, including calibrated differential privacy, the Paillier homomorphic encryption, and secure aggregation, which guarantee that the updates of the models are kept secure during the training pipeline. A node-weighting program is integrated so as to stabilize convergence in the situation where the data distribution is heterogeneous by giving priority to high-quality institutional contributions. Health-FedNet was tested on the MIMIC-III clinical database to predict diabetes and hypertension in a simulated environment of multiple hospitals under realistic conditions. The results of the experiment obtained after five independent runs prove that the model has reached an accuracy of 92%, and AUC-ROC is equal to 0.94, with the confidence interval showing the relevance of these findings at 95 percent. Paired t-tests ( p < 0.01) have statistically validated that Health-FedNet is 12% more predictive than centralized and baseline federated (meaning less communication overhead produces a 41.6% decrease in communication overhead). Privacy tests show that the suggested approach will lower the membership inference risk by 20 to 5%. The framework is compliant with HIPAA and GDPR and proves to be robust in the presence of noisy or imbalanced clinical data. Health-FedNet gives a viable basis for safe federated healthcare analytics and has high potential to be implemented in distributed hospital information systems.