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September 10, 2025

Federated Learning in Healthcare: A Privacy-Preserving Approach to Medical AI

Key Points

Federated learning enables AI in healthcare by keeping patient data confidential while enhancing collaboration.
Strong evidence shows that federated learning can be applied effectively in cancer diagnosis and COVID-19 research.
Challenges, including data heterogeneity and security risks, hinder the widespread adoption of federated learning in medical AI.
Hybrid solutions, integrating federated learning with encryption and supportive policies, are essential for its success in healthcare.

Abstract

Artificial intelligence (AI) is reshaping healthcare by advancing predictive analytics, medical imaging, drug discovery, and personalized treatment. Yet, progress is conAstrained by the need for large, diverse datasets and the challenges of privacy, regulation, and institutional data silos. Federated learning (FL) has emerged as a privacypreserving solution that trains models locally and shares parameters instead of raw data, enabling collaboration without compromising confidentiality. FL has been applied in cancer diagnosis, COVID-19 research, genomics, and electronic health records, showing strong potential for medical innovation. However, technical issues—such as data heterogeneity, communication overhead, model convergence, and security risks—along with economic and organizational barriers like cost, skills, and interoperability, slow adoption. Despite these challenges, FL is both a technological breakthrough and a strategic enabler for collaborative healthcare research. Hybrid approaches integrating FL with homomorphic encryption, differential privacy, and blockchain governance, combined with supportive policies and international cooperation, will be vital to realizing its full potential in healthcare.

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