Securing Federated Learning in Medical Image Analysis: A Systematic Review of Privacy Threats and Defense Mechanisms

Federated Learning (FL) is a cutting-edge method in the medical imaging field that allows hospitals to collaboratively build models without revealing patient data. Nevertheless, FL is still vulnerable to numerous security and privacy issues, including, but not limited to, data poisoning, Byzantine attacks, and inference attacks. The existing literature has only partly dealt with this topic by focusing either on particular threats or on mitigation strategies, thus leaving the overall comprehension of the problems and their solutions in medical imaging as inadequate. The main threats to FL are systematically classified in this systematic review, with two major vulnerable assets, medical data and model parameters, being pointed out. We review existing countermeasures based on cryptographic techniques, secure aggregation, perturbation methods, and security protocols with an emphasis on their efficiency in ensuring patient privacy and model integrity. We also discuss the impact of FL in medical imaging, where it serves as a tool for privacy preservation and has the potential to improve diagnostic accuracy. Data heterogeneity, communication overhead, and lack of standardization are key challenges that are considered, as well as potential future research paths to explore for solving these problems. Overall, the systematic review reveals that, although federated learning provides enormous privacy-preserving benefits in medical imaging, its actual implementation needs to be very cautious regarding the merging of very strong security measures and the use of standard protocols so that the weaknesses are reduced, and the reliability of the diagnostics is increased.