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October 10, 2025International Innovative Research Journal of Engineering and Technology

Federated Learning Models for Privacy-Preserving Medical Image Analysis

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Authors

SKSuman KumarVSVinoth Kumar S

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Overview

Federated learning enhances data privacy in medical image analysis, suggesting scalable AI solutions in healthcare.

Key Points

  • Federated learning models maintain patient data privacy while providing diagnostic accuracy, enhancing healthcare outcomes.
  • Results indicate that federated learning approaches perform comparably to centralized methods, safeguarding privacy throughout.
  • The study proposes solutions like personalized models and differential privacy to address challenges in federated learning use.
  • Adopting federated learning can potentially revolutionize medical imaging, indicating a strong future for ethical AI applications.

Cite This Study

Kumar et al. (2025) studied this question.

synapsesocial.com/papers/68e861857ef2f04ca37e39d8https://doi.org/10.32595/iirjet.org/v10i4.2025.226
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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 for Medical Image Analysis: Privacy-Preserving Paradigms and Clinical Challenges2025 · 4 citations
  2. 2Federated Learning for Large Models in Medical Imaging: A Comprehensive Review2025
  3. 3Federated Learning in Healthcare: A Privacy-Preserving Approach to Medical AI2025
  4. 4Federated Learning for Secure and Privacy-Preserving Medical Collaboration Across Multi-Cloud Healthcare Systems2024 · 2 citations
  5. 5Implementing federated learning with privacy-preserving encryption to secure patient-derived imaging and sequencing data from cyber intrusions2025 · 12 citations