Federated Learning for Privacy-Preserving Diabetes Prediction: Challenges, Solutions, and Future Directions

Federated Learning (FL) has emerged as a revolutionary approach in healthcare, facilitating decentralized machine learning model training across multiple institutions while mitigating key challenges related to data privacy, security, and centralization. This review delves into the application of FL for diabetes prediction, focusing on its capacity to leverage distributed datasets from diverse, geographically dispersed sources without compromising the confidentiality of patient data. We provide a technical overview of FL’s core principles, emphasizing the collaborative training of models locally and aggregating them into a global model. Furthermore, we investigate the incorporation of sophisticated machine learning frameworks, encompassing Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks, and assess their performance in diabetes prediction tasks. The review critically evaluates the state of FL in healthcare, highlighting existing challenges as communication overhead, model convergence, and handling of data heterogeneity. Furthermore, we propose potential solutions to enhance FL’s scalability, model accuracy, and real-world applicability in healthcare. By providing a comprehensive analysis of current trends and future directions, this paper aims to provide a technical foundation for the development of privacy-preserving, high-performance predictive models in diabetes and broader healthcare domains.