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BF2GAN: A Fused Federated Learning-Based Biometric Enabled Deep Learning Approach for Secure Medical Image Sharing in

Zeeshan Ahmed Mohammed1, D Rajeshwari2, Shanavaz Mohammed3

  • 1Department of Information Technology, University of the Cumberlands, 6178 College Station Drive, Williamsburg, KY, 40769, USA. dr.zeeshan.mohamm@gmail.com.

Journal of Imaging Informatics in Medicine
|April 27, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces a Biometric Fused Federated Generative Adversarial Network (BF2GAN) for secure medical image sharing. BF2GAN enhances security and efficiency by combining biometrics with federated learning, improving data protection and simplifying encryption.

Area of Science:

  • Medical Imaging
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Medical images contain sensitive patient data requiring robust security measures.
  • Existing encryption methods face challenges with complexity, compression, and real-time performance.
  • Unauthorized access to medical images poses significant privacy risks.

Purpose of the Study:

  • To propose a novel method, Biometric Fused Federated Generative Adversarial Network (BF2GAN), for enhanced medical image security.
  • To address limitations of current encryption schemes in medical image sharing.
  • To improve data integrity and reduce data exposure risks during collaborative training.

Main Methods:

  • Implementation of federated learning (FL) for decentralized, collaborative model training.
Keywords:
Biometric informationCloud networksFederated learningGenerative Adversarial NetworkMedical image sharing

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  • Integration of biometric information for user verification and secure key generation.
  • Utilizing Generative Adversarial Networks (GANs) within a federated framework.

    • Main Results:

    • BF2GAN achieved efficient encryption (3.08s) and decryption (3.9s) times.
    • High image quality was maintained, evidenced by a Structural Similarity Index Measure (SSIM) of 0.975 and Feature Similarity Index (FSI) of 0.82.
    • The method demonstrated strong performance with a Peak Signal-to-Noise Ratio (PSNR) of 58.38 dB and minimal memory usage (280.87 KB).

    Conclusions:

    • BF2GAN offers a simplified, secure encryption process for medical images, reducing reliance on complex key management.
    • The method enhances data integrity and privacy through decentralized training and biometric authentication.
    • BF2GAN presents a superior alternative to state-of-the-art methods for secure medical image sharing.