Reviews Highlight Blockchain’s Role in Medical Data Sharing and Federated Learning

Two reviews examine blockchain-based medical data sharing initiatives and blockchain-based federated learning in health care, finding that governance and transaction mechanisms are particularly influential in sustaining initiatives and that BCFL frameworks combine the decentralized trust and auditability of blockchain with the privacy-preserving collaborative learning capabilities of federated learning. The studies describe secure and efficient sharing of sensitive medical data across institutions as a major challenge because of privacy concerns, data silos, regulatory restrictions, data security risks, and single points of failure in centralized systems.

One review identified 42 initiatives, categorizing them based on ownership, governance, business, incentive, transaction, and sustainability models. The base model, run at an inclusion threshold of 0.65, identified multiple configurations associated with sustained initiative activity, highlighting the role of governance mechanisms and transaction structures in supporting long-term viability. The sensitivity analysis conducted across multiple thresholds found that at 0.80, only two configurations remained, representing the most consistent pathways to sustained activity.

That analysis found a range of governance, ownership, business, and sustainability models, with no single structural configuration guaranteeing long-term viability. The findings suggest that governance and transaction mechanisms are particularly influential in sustaining initiatives, often compensating for the absence of strong business or sustainability models. The scope was limited to initiatives identified through available documentation and snowball sampling, and the results underscore the need for further research into the interplay between governance structures, financial models, and long-term sustainability in medical data sharing.

The systematic literature review of blockchain-based federated learning said the exponential growth of medical data and advancements in artificial intelligence have accelerated the development of data-driven health care, but the secure exchange and control of sensitive health information have become central concerns. The review searched studies from 2018 to 2025 and, after automated deduplication and multistage manual screening, included over 100 high-quality papers covering theoretical foundations, system architectures, application domains, limitations, and future directions.

According to the review, BCFL enhances data security, supports privacy-preserving cross-institutional collaboration, and facilitates practical applications in health care, including medical data sharing, Internet of Medical Things, public health surveillance, and telemedicine. Applications span across cross-institutional medical data sharing, Internet of Medical Things, epidemic forecasting, and telemedicine, while architectures including fully coupled, flexibly coupled, and loosely coupled models offer varying trade-offs between efficiency, scalability, and security.

The review said this integration mitigates risks such as model tampering, data leakage, and a lack of incentives in federated systems. It added that federated learning allows institutions to collaboratively train models without exchanging raw data, while strict legal and regulatory requirements such as HIPAA and GDPR must be met when handling personal data. Despite current technical and practical challenges, the review said BCFL demonstrates strong potential to support precision medicine, global health data collaboration, and large-scale AI deployment in health care.