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Federated Learning in Neurology: Bridging Data Privacy and Artificial Intelligence for Brain Health.

Sahar Soltanieh1, Farzad Khalvati1,2, E Ann Yeh1,3

  • 1Division of Neuroscience and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada.

Seminars in Neurology
|December 29, 2025
PubMed
Summary

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Federated learning (FL) enables collaborative AI model training for neurological disorders without sharing patient data. While promising for tasks like tumor segmentation, widespread clinical use requires overcoming technical and regulatory hurdles.

Area of Science:

  • Neurology
  • Artificial Intelligence
  • Machine Learning

Background:

  • Neurological disorders impact millions globally, posing challenges for AI clinical translation due to data fragmentation and privacy concerns.
  • Federated learning (FL) offers a solution by enabling collaborative model training across institutions without raw patient data sharing.
  • FL is increasingly explored for diverse neurological applications, from neuroimaging to electronic health records.

Purpose of the Study:

  • To review federated learning applications in neurology between 2020 and 2025.
  • To analyze real-world FL deployments, algorithmic trends, and barriers to clinical translation.
  • To propose strategies for enhancing FL adoption in precision neurology.

Main Methods:

  • Systematic review of federated learning applications in neurology.

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  • Analysis of studies focusing on neuroimaging, electrophysiology, and electronic health records.
  • Examination of technical, regulatory, and organizational challenges.

    • Main Results:

    • FL shows feasibility in neuroimaging (e.g., brain tumor segmentation, MS lesion detection) and EHR-based predictive modeling.
    • Verified clinical implementations of FL in neurology remain limited.
    • Key barriers include privacy, data standardization, regulatory compliance, and operational scalability.

    Conclusions:

    • Federated learning holds significant potential for advancing precision neurology while preserving patient privacy.
    • Strategies such as privacy-preserving techniques, standardized infrastructure, and cross-disciplinary collaboration are crucial for clinical translation.
    • Bridging technical innovation with regulatory and operational considerations is essential for widespread FL adoption.