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This review article focuses on how federated learning can transform AI applications in healthcare by facilitating collaboration while protecting patient data.

January 24, 2026

Federated Learning in Healthcare: From Research to Real-World Deployment

Key Points

This review article focuses on how federated learning can transform AI applications in healthcare by facilitating collaboration while protecting patient data.
Reviewed developments in federated learning relevant to biomedicine.
Discussed privacy solutions like differential privacy and secure aggregation.
Analyzed practical challenges in implementing federated learning in healthcare settings.
Federated learning enables institutions to collaborate without sharing sensitive data.
Identified strategies to manage data variability across institutions.
Outlined the necessity for scalable infrastructure to support federated learning models.

Abstract

Artificial intelligence (AI), including deep and traditional machine learning, holds great promise for advancing biomedical research and healthcare. However, most AI studies remain academic in nature and rarely transition into clinical practice, largely due to limited access to diverse real-world datasets. Centralized learning, the traditional approach to multi-institutional collaboration, is hindered by privacy, legal, and logistical barriers. Federated learning (FL) offers a decentralized alternative, enabling institutions to collaboratively train models without sharing sensitive patient data. This article reviews key algorithmic, privacy, and practical developments in FL for biomedical engineering, including strategies to handle non-identical data distributions and safeguard privacy through differential privacy, secure aggregation, and confidential computing. We also discuss current limitations and considerations for the need of scalable, interoperable infrastructures. FL represents a paradigm shift toward building generalizable, equitable, and clinically impactful AI models. Realizing this vision requires continued advances, such as FL-as-a-service platforms and regulatory-aligned workflows that support persistent and trustworthy model deployment to truly realize AI's promise in patient care.

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Federated Learning in Healthcare: From Research to Real-World Deployment

Key Points

Abstract

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