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September 10, 2025

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

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Authors

KKKalyan KasturiJNJ. Naresh

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Overview

Federated learning improves collaboration in healthcare by preserving patient privacy and sharing AI insights, highlighting its potential in applications like genomics and cancer diagnosis.

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.

Cite This Study

Kasturi et al. (2025) studied this question.

synapsesocial.com/papers/68c23d3ab210217d6478cc54https://doi.org/10.63856/fm5jw605
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Federated Learning in Healthcare: From Research to Real-World Deployment2026 · 8 citations
  2. 2Federated Learning for Secure and Privacy-Preserving Medical Collaboration Across Multi-Cloud Healthcare Systems2024 · 2 citations
  3. 3Federated Learning Models for Privacy-Preserving Medical Image Analysis2025 · 1 citations
  4. 4Federated Learning for Medical Image Analysis: Privacy-Preserving Paradigms and Clinical Challenges2025 · 4 citations
  5. 5Federated Learning for Privacy-Preserving Artificial Intelligence: Challenges, Opportunities, and Future Directions2025