Federated Learning Techniques for Secure and Accurate Diabetes

Abstract

Diabetes is a growing global health concern, with a significant rise in prevalence over the past few decades. Traditional machine learning approaches for diabetes prediction often involve centralizing sensitive patient data, which poses significant privacy and security risks