What question did this study set out to answer?

This research aims to develop a framework for managing risks associated with federated learning in academic medical centers.

March 13, 2026JMIR AI1 citationsOpen Access

A Pragmatic Framework for Federated Learning Risk and Governance in Academic Medical Centers

DBDaniel BottomlyOregon Health & Science University BBBridget BarnesOregon Health & Science University KMKuli MavuwaOregon Health & Science University

Puntos clave

This research aims to develop a framework for managing risks associated with federated learning in academic medical centers.
Review of relevant international standards for AI risk management.
Development of a risk differentiation framework for federated learning.
Creation of a federated learning risk matrix and governance artifacts.
Proposed tools enhance data privacy and security for collaborative model training.
Framework aligns with established risk management standards.
Identified governance gaps that need addressing to ensure effective implementation of federated learning.

Resumen

With the rapid development of artificial intelligence (AI), particularly large language models, there is growing interest in adopting AI approaches within academic medical centers (AMCs). However, the vast amounts of data required for AI and the sensitive nature of medical information pose significant challenges to developing high-performing models at individual institutions. Furthermore, recent changes in government funding priorities may result in the decentralization of biomedical data repositories that risk creating significant barriers to effective data sharing and robust model development. This has generated significant interest in federated learning (FL), which enables collaborative model training without transferring data between institutions, thereby enhancing the protection of proprietary and sensitive information. While FL offers a crucial pathway to enable multi-institutional AI development while maintaining data privacy, it also exposes AMCs to novel governance, security, and operational risks that are not fully addressed by existing procedures. In response, this manuscript provides a perspective grounded in both leading international standards (NIST AI RMF National Institute of Standards and Technology Artificial Intelligence Risk Management Framework, International Organization for Standardization (ISO) and International Electrotechnical Commission (IEC) 42001) and in the real-world governance experience of AMC leadership. We present a risk differentiation framework, an FL risk matrix, and a set of essential governance artifacts-each mapped to key institutional challenges and reviewed for alignment with core standards but offered as pragmatic, illustrative guides rather than prescriptive checklists. Together, these tools represent a novel resource to support AMC security, privacy, and governance leaders with standards-informed, context-sensitive tools for addressing the evolving risks of FL in biomedical research and clinical environments.

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