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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.

Hospitals seek to compare performance on operational metrics and quality indicators, but sharing granular patient-level, unit-level, or institution-level data raises privacy, legal, reputational, and competitive concerns. Privacy-preserving analytics has therefore become increasingly relevant for health systems that need collective insight without centralized pooling of sensitive data. This systematic review examines privacy-preserving models used for operational benchmarking, quality monitoring, resource planning, and multi-institutional performance comparison in healthcare. The review focuses on federated analytics, federated learning, secure multi-party computation, differential privacy, homomorphic encryption, and secure aggregation. A PRISMA 2020-compliant search was designed for PubMed, Scopus, IEEE Xplore, and Web of Science covering studies published from 2017 to 2025. Records were screened by two reviewers, and eligible studies were synthesized narratively by application domain, privacy-preserving method, implementation maturity, and operational relevance. Federated learning and secure aggregation dominated the literature, with more recent work increasingly combining federated workflows with differential privacy, homomorphic encryption, or governance frameworks. Most studies addressed clinical prediction or biomedical analytics, while fewer directly examined operational benchmarking, capacity planning, or routine multi-hospital performance comparison. The technical foundations for privacy-preserving federated analytics are increasingly mature, but their translation into routine healthcare operations remains early. Evidence is strongest for multi-site clinical modeling and weakest for sustained operational benchmarking, resource planning, and governance-tested deployment.

Accurate prediction of demand for emergency, imaging, pharmacy, laboratory, and inpatient services is critical for hospital planning. However, forecasting models are typically built separately by department or institution, which limits their ability to learn from shared demand patterns. Hospitals generate rich operational demand streams, but patient-level data cannot usually be pooled across organizations. This creates a need for collaborative forecasting methods that preserve institutional control over sensitive operational records. This article proposes a federated multi-task learning framework for predicting service demand across multiple hospital units. The framework trains a shared predictive model across hospitals while each institution contributes only protected model updates. The framework includes local data adapters, a shared temporal learning backbone, task-specific forecasting heads, a federated aggregation layer, differential privacy mechanisms, and site-specific personalization modules. Together, these components support collaborative forecasting without transferring patient-level operational data. The framework could improve demand prediction by learning common temporal patterns across hospitals and service lines. It would also support local adaptation, reduce duplicated model development, and preserve data confidentiality. A privacy-preserving, collaborative approach to hospital demand forecasting could become a core infrastructure for multi-site operational coordination. Federated multi-task learning offers a practical conceptual foundation for such a system.

Patient access performance—scheduling efficiency, referral completion, wait times, and appointment attendance—varies widely across healthcare organizations. These organizations rarely learn from each other because operational data are sensitive, locally governed, and often competitively protected. Isolated access analytics limit the discovery of generalizable patterns and prevent hospitals from learning from peer institutions with different patient populations and workflows. No current operational platform fully enables multi-hospital learning about patient access without exposing patient-level scheduling, referral, and attendance data. This article proposes a privacy-preserving AI platform that uses federated learning and secure aggregation to support cross-hospital modeling of scheduling demand, referral completion, appointment lead time, and no-show risk. Raw data remain within each participating hospital, while only protected model updates or aggregate statistics contribute to shared learning. The platform consists of local data adapters, standardized access-feature pipelines, a federated model trainer, a secure aggregation layer, differential privacy controls, and local operational dashboards. Each hospital receives a shared model that can be adapted locally while preserving institutional data control. The framework could enable hospitals to benefit from broader operational learning while maintaining confidentiality, competitive neutrality, and governance accountability. It would be expected to support more consistent access analytics across heterogeneous health systems without requiring centralized pooling of sensitive records. Privacy-preserving AI could support a new collaborative analytics paradigm for patient access and healthcare operations. Such platforms should be evaluated through multi-institutional pilots that assess technical feasibility, governance readiness, privacy protection, and operational usefulness.