Securing Diabetic Prediction with Federated Learning and Homomorphic Encryption

The rising worldwide impact of diabetes emphasizes the need for precise and personalized prediction models. However, the ethical and legal challenges associated with handling sensitive healthcare data necessitate innovative solutions that balance predictive accuracy with individual privacy. This research investigates the novel integration of two cutting-edge technologies, Federated Learning (FL) and Homomorphic Encryption (HE), to advance field of diabetic prediction while prioritizing data privacy. In this study, we give a detailed exploration of the Federated Learning and Homomorphic Encryption framework for diabetic prediction. We discuss the technical implementation, evaluate the system's predictive performance, and assess its ability to protect sensitive patient information. Our experiments reveal that the proposed approach achieves good prediction accuracy while preserving individual privacy, thereby offering a promising avenue for the development of secure and collaborative diabetic prediction models.