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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.

Detecting rare diseases often requires data from multiple institutions due to the scarcity of cases at individual hospitals. Centralizing data is not feasible due to privacy, consent, and jurisdictional issues. Federated learning enables model training across hospitals without transferring raw data, but it lacks formal privacy guarantees. Model updates can still leak information, and aggregation servers may compromise privacy if they handle unprotected data. This article presents a conceptual framework combining federated learning, differential privacy, and secure multi-party computation for rare disease detection across 50+ international hospitals. The system addresses data scarcity, regulatory fragmentation, and network heterogeneity. Each hospital trains a local model, applies differential privacy to updates, and shares encrypted updates via an aggregation protocol. Non-colluding servers compute global updates without accessing plaintext data. Differential privacy reduces the impact of individual patient data, while secure multi-party computation ensures privacy at the aggregation layer. These methods enable a privacy-preserving approach to federated learning for rare disease collaboration. The proposed framework enables multi-continental rare disease detection without centralizing patient data, offering a privacy-preserving model for future consortia.

Fall risk in aging populations is a modifiable health concern, with mobility patterns changing over time due to factors like frailty, comorbidities, and medication. Smartwatch accelerometers provide a privacy-sensitive way to monitor gait and movement outside clinical settings. However, federated learning, which supports privacy by keeping sensor data local, faces challenges in aging populations due to concept drift from gradual mobility decline, which can invalidate static models. This article proposes a federated continual learning framework to adaptively maintain fall risk prediction models using smartwatch data. The system includes local models that combine feature extraction with temporal sequence modeling, continual learning to prevent forgetting, and a federated server for privacy-preserving coordination. It aims to support personalized fall risk monitoring, reduce concept drift, and enable scalable deployment in senior care settings, with clinical validation necessary for real-world assessment.

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.