Privacy-Preserving Federated Learning in Medical AI: A Systematic Review of Techniques, Challenges, and the Clinical Deployment Gap
Abstract
Federated learning enables collaborative medical AI development across institutions without centralized data sharing, addressing critical privacy concerns in healthcare. This systematic review examines privacy-preserving techniques, technical challenges, and the significant deployment gap where 95% of federated learning research fails to reach clinical practice. We analyze differential privacy, homomorphic encryption, and secure multi-party computation approaches across medical applications from 2023-2025. Key findings reveal that while federated learning with differential privacy achieves comparable performance to centralized training in specific domains like medical imaging, significant barriers persist including data heterogeneity, communication overhead, and regulatory compliance challenges. The review identifies critical gaps between research innovations and clinical deployment, providing a roadmap for practical implementation of privacy-preserving federated learning systems in healthcare environments.
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
Read the paper
Related studies
- Active Learning during Federated Learning for Object Detection
- Fairness-aware federated learning
- Decentralized Federated Learning
- Building a Personally Identifiable Information Recognizer in a Privacy Preserved Manner Using Automated Annotation and Federated Learning
- Preservation of Patient Level Privacy: Federated Classification and Calibration Models
- Efficient explainable federated learning
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.