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Enhanced Privacy in Optical Image Restoration: A Federated Deep Memory-Integrated Neural Network Approach
  • Muhammad Jahanzeb Khan,
  • Suman Rath,
  • Muhammad Hassan Zaib
Muhammad Jahanzeb Khan
University of Nevada

Corresponding Author:[email protected]

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Suman Rath
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Muhammad Hassan Zaib
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In the realm of remote sensing images, restoration and privacy preservation stand as dual challenges. While the intricate characteristics of these images render conventional restoration methods inadequate, concerns regarding data privacy pose a significant barrier to their optimal utilization. Addressing this multifaceted challenge, this study synergizes the Deep Memory Connected Network (DMCN) with federated learning, enabling data-driven model improvements without direct access to the raw image data. This federated approach, while bolstering data privacy, introduces inherent noise into the learning process. To counteract this, techniques such as Gaussian image denoising were employed, ensuring restoration quality. Notably, the federated DMCN exhibited commendable performance, showcasing only a marginal accuracy degradation in the face of noise. Downsampling Units, integral to DMCN, further contributed by reducing computational overheads. Comprehensive evaluations on remote sensing datasets underscore the promise of this federated approach, balancing data privacy with restoration fidelity, and charting a viable path for future applications.
05 Apr 2024Submitted to TechRxiv
08 Apr 2024Published in TechRxiv