Privacy-Preserving Federated Learning for Predictive Healthcare on Heterogeneous Medical Data

In modern healthcare, preserving patient privacy while utilizing data for predictive analytics remains a critical challenge. Federated Learning (FL) provides an efficient strategy by providing decentralized model training around different institutions without requiring the transfer of sensitive patient in- formation. This study investigates the implementation of privacy- preserving FL for predictive healthcare, specifically targeting heart disease, kidney disease, and diabetes. Each client in the FL network trains a neural network locally on disease-specific datasets, followed by model aggregation using ensemble learning and knowledge distillation to enhance global model performance while ensuring data confidentiality. Empirical results reveal high predictive accuracies: 79.39% for heart disease, 92.45% for kidney disease, and 70.36% for diabetes. The ensemble models, which synthesize knowledge between clients, maintain these accuracy levels, underscoring the efficacy of FL in delivering robust, privacy-preserving diagnostic models. These findings demonstrate the potential of FL to securely integrate data from multiple healthcare institutions, offering a pathway for scalable, decentralized predictive models in clinical settings. Further studies include the incorporation of more advanced deep learning architectures, the development of personalized federated models tailored to individual patient populations, and the extension of FL to a wider array of medical conditions. Additionally, addressing challenges related to model interpretability and managing client heterogeneity will be essential for broader implementation. This work lays the groundwork for the advancement of privacy- preserving AI solutions in healthcare, with profound implications for regulatory-compliant machine learning applications.