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April 1, 2026Open Access

Federated Learning Frameworks for Privacy-Preserving Diagnostic Imaging in Multi-Site Hospital Clusters

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Authors

PAPriyanka K., Naveen R., Zoya A.

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Overview

Framework demonstrates improved diagnostic accuracy in oncology without compromising data privacy.

Key Points

  • The aim is to develop a federated learning framework for training diagnostic models in a privacy-preserving manner.
  • Implemented federated learning to exchange model gradients instead of raw patient images.
  • Integrated a differential privacy layer to enhance security and privacy of local updates.
  • Evaluated performance using high-resolution MRI datasets in a simulated hospital cluster.
  • Achieved diagnostic accuracy within 2.5% of centrally trained models.
  • Ensured 100% compliance with local data residency requirements.
  • Demonstrated potential for scalable multi-institutional clinical research.

Cite This Study

Priyanka K., Naveen R., Zoya A. (2026) studied this question.

synapsesocial.com/papers/69ccb76c16edfba7beb89628https://doi.org/10.5281/zenodo.19337205
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Also Consider

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  1. 1Regulatory-orientedDeep federated learning framework for multi-hospital medical imaging: privacy-preserving, explainable, and generalizable diagnosis2026
  2. 2Federated Learning Models for Privacy-Preserving Medical Image Analysis2025 · 1 citations
  3. 3Advanced Privacy-Preserving AI Techniques for Distributed Disease Diagnosis2026
  4. 4Federated Learning for Privacy-Preserving Medical Diagnosis on Edge Devices: A Comprehensive Research Framework2025
  5. 5Federated Learning for Large Models in Medical Imaging: A Comprehensive Review2025