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

Abstract

Neurological disorders affect hundreds of millions globally, yet translating artificial intelligence (AI) advances into clinical practice remains challenging due to fragmented, privacy-sensitive datasets. Federated learning (FL) has emerged as a promising paradigm, enabling collaborative model training across institutions without sharing raw patient data. This review synthesizes FL applications in neurology from 2020 to 2025, spanning neuroimaging, electrophysiology, and electronic health records. We analyze real-world deployments, highlight algorithmic trends, and discuss technical, regulatory, and organizational barriers to clinical translation. While FL demonstrates feasibility in tasks such as brain tumor segmentation, multiple sclerosis lesion detection, and electronic health record-based predictive modeling, verified clinical implementations remain scarce. We outline strategies to enhance adoption, including privacy-preserving techniques, standardized infrastructures, domain-adaptive algorithms, and cross-disciplinary collaboration. By bridging technical innovation with regulatory compliance and operational scalability, FL holds significant potential to advance precision neurology while safeguarding patient privacy.

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