Federated Learning in the Diagnosis of Medical Diseases: A Survey

With the advent of digital healthcare, the volume of medical imaging data has grown exponentially, necessitating advanced analytics for improved diagnostics and treatment planning. Federated learning (FL) emerges as a promising solution to leverage this data while preserving patient privacy. This comprehensive survey explores the application of federated learning in medical image analysis, focusing on its utility across various disease types. We begin by outlining the fundamental principles of federated learning and its significance in the context of medical image analysis, emphasizing the privacy-preserving nature of FL that aligns with stringent healthcare regulations. The survey then delves into the applications of FL across different medical domains, including Alzheimer’s Disease, COVID-19, skin diseases, and Diabetic Retinopathy, highlighting how FL enables the analysis of decentralized data without compromising patient confidentiality. We also discuss the datasets used in FL research. The review further addresses the challenges inherent in FL.