SoK: Federated Learning and Unlearning for Medical Image Analysis

Abstract

Medical image analysis is a critical component of modern healthcare, enabling accurate disease diagnosis and effective patient treatment. However, the process is fraught with challenges, including inter- and intra-observer variability, time constraints, and data-related issues such as privacy, heterogeneity and accessibility. Within this framework, Federated Learning (FL) has emerged as a promising solution, allowing collaborative model training across distributed healthcare entities without sharing sensitive patient data. This study provides a comprehensive Systematization of Knowledge (SoK) review of FL and its extension, Federated Unlearning (FU), within the context of medical image analysis. FL enables privacy-preserving, decentralised model training, while FU addresses the ‘Right To Be Forgotten’, ensuring compliance with data protection regulations like GDPR and HIPAA. We explore the opportunities and challenges of FL and FU, detailing their methodologies, frameworks, datasets, and evaluation metrics. The review highlights the potential of FL and FU to enhance diagnostic accuracy, improve patient care, and foster trust in AI-driven healthcare systems. We also identify research gaps and propose future directions for advancing FL and FU in medical imaging, emphasising the need for interdisciplinary collaboration and the development of dedicated frameworks. Thus, this study aims to bridge the gap between theoretical advancements and practical applications, paving the way for more robust and privacy-compliant AI models in healthcare.