Rare diseases are challenging for AI development due to sparse patient populations, fragmented expertise, and strong inter-site variability, making federated learning a promising privacy-preserving solution for multi-institutional model training. This systematic review evaluates federated learning approaches for rare disease diagnosis and related data-scarce clinical settings, with emphasis on handling extreme data scarcity, class imbalance, heterogeneity, and privacy constraints. A PRISMA 2020-compliant search of PubMed, IEEE Xplore, Scopus, Web of Science, and arXiv (2017–2025) identified 2,015 records, with 56 studies included after screening. The most commonly used strategies included FedProx-based optimization, personalized federated learning, class-aware aggregation, generative data augmentation, and domain adaptation techniques. Overall, standard federated averaging is often insufficient under severe scarcity and distribution shift, while hybrid approaches combining personalization, augmentation, and domain adaptation show greater promise for improving performance in rare disease applications.
Federated and decentralized machine learning offer the potential to extract valuable healthcare insights from siloed data without requiring the centralization of sensitive patient records, addressing long-standing privacy and governance challenges. This critical review assesses federated learning in healthcare through three lenses: privacy-preserving technologies, incentive mechanisms, and regulatory compliance frameworks. It examines whether the claims in existing literature are substantiated by real-world evidence from healthcare settings. The review reveals considerable enthusiasm for federated learning but identifies gaps, including incomplete implementation of privacy technologies, theoretical incentive mechanisms, and regulatory compliance often assumed but not validated. Additionally, real-world deployments are limited in scale and duration. The review concludes that the gap between federated learning's theoretical potential and clinical application remains significant, with overstated privacy claims and a lack of established frameworks for incentives and compliance.
Clinical Intelligence Research Press