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

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Federated learning (FL) enables collaborative AI model development across academic medical centers (AMCs) without sharing sensitive patient data. This approach addresses data privacy concerns but introduces new governance and security risks requiring specialized management frameworks.

Keywords:
academic medical centersartificial intelligencefederated learninggovernancesecurity and privacy

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Area of Science:

  • Artificial Intelligence in Medicine
  • Federated Learning
  • Health Data Governance

Background:

  • Academic medical centers (AMCs) face challenges in developing high-performing AI models due to data volume and privacy concerns.
  • Decentralization of biomedical data repositories may hinder effective data sharing and AI model development.
  • Federated learning (FL) emerges as a solution for collaborative AI training while preserving data privacy.

Purpose of the Study:

  • To address the novel governance, security, and operational risks associated with FL implementation in AMCs.
  • To provide a standards-informed framework for managing FL risks in biomedical research and clinical settings.
  • To support AMC leaders in navigating the complexities of multi-institutional AI development.

Main Methods:

  • Developed a risk differentiation framework and an FL risk matrix.
  • Created essential governance artifacts mapped to institutional challenges.
  • Aligned proposed tools with international standards (NIST AI RMF, ISO/IEC 42001) and AMC leadership experience.

Main Results:

  • Presented pragmatic, illustrative guides for FL risk management, not prescriptive checklists.
  • Offered tools grounded in leading international standards and real-world governance experience.
  • Facilitated alignment of FL governance with key institutional challenges.

Conclusions:

  • FL is a critical pathway for multi-institutional AI development in healthcare, balancing innovation with data privacy.
  • Existing procedures are insufficient for managing FL-specific risks in AMCs.
  • The proposed framework and artifacts offer a novel resource for AMC leaders to manage evolving FL risks effectively.