Privacy-Preserving Image Deblurring with Federated Learning through an Adaptive Framework for Cloud-Assisted Devices
Keywords:
Deblurring, Wiener filter, Encrypted image storage, Federated learning, Edge device, Data privacy.Abstract
This research introduces a cutting-edge approach to image deblurring while prioritizing data privacy. Leveraging federated learning and incorporating advanced techniques such as the wiener filter, encrypted image storage, and cloud-based infrastructure, the proposed framework enables collaborative model training across distributed edge devices while preserving the confidentiality of sensitive data. The framework utilizes a cloud server and database for efficient data management and storage, ensuring seamless integration and scalability. By employing federated learning, individual devices participate in model training without compromising data privacy, while encrypted image storage safeguards against unauthorized access. The wiener filter enhances the deblurring process, optimizing image quality and accuracy. Through federated learning, the framework achieves collaborative model training across diverse edge devices, effectively distributing computational tasks while minimizing data exposure. The integration of encrypted image storage ensures robust protection of sensitive data, mitigating privacy concerns associated with centralized data storage. The utilization of the wiener filter enhances image deblurring performance, resulting in improved image quality and sharper outputs. The framework offers a holistic solution for privacy-preserving image deblurring, combining state-of-the-art techniques with federated learning to achieve superior results while maintaining data privacy and security.Top of Form
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