In federated learning (FL) systems, the Cheon-Kim-Kim-Song (CKKS) homomorphic encryption scheme is crucial for preserving privacy while enabling computations on encrypted decimal numbers. However, efficient search operations on CKKS encrypted data remain a significant challenge. This paper addresses this gap by introducing a novel search algorithm optimized for CKKS ciphertexts, significantly reducing client-server interactions in decentralized environments. Our approach integrates parallel computing techniques and a balanced binary tree structure to handle complex datasets like CIFAR10 and MNIST efficiently. We also demonstrate the algorithm's applicability to Convolutional Neural Networks (CNNs) for feature selection and privacy-preserving inference. Comprehensive evaluations show our method's scalability and practical efficiency under various network latencies, advancing privacy-preserving data processing in FL applications without compromising computational efficiency or model accuracy.

\cite{Zafar_2019}This research addresses the dual challenges of image restoration quality and data privacy in optical remote sensing. Traditional restoration methods often fall short due to the complex nature of remote sensing images, and data privacy concerns further complicate the use of advanced techniques. By integrating the Deep Memory Connected Neural Network (DMCN) with the Data-Decoupled Federated Learning (DDFL) framework, our approach enables significant improvements in image restoration without requiring direct access to sensitive raw data. This method not only enhances data privacy by leveraging federated learning principles but also incorporates advanced techniques like Gaussian image denoising to maintain high restoration quality despite potential noise introduced by the federated process. The performance of the federated DMCN, particularly on the UCMERCED dataset, demonstrates minimal accuracy degradation, even in the presence of noise, while the strategic use of Downsampling Units within DMCN optimizes computational efficiency. Our comprehensive evaluations reveal the effectiveness of this approach in balancing data privacy with the need for high-quality image restoration, suggesting a promising direction for future advancements in remote sensing applications.

Recent advancements in privacy-preserving artificial intelligence (AI) have paved the way for enhanced privacy in computational processes. A standing challenge, however, is the robust privacy preservation in AI algorithms, especially when integrated into edge devices and Internet-of-Thing (IoT) infrastructures. Most prevailing solutions have adopted traditional encryption methods which, though secure, often introduce significant overhead and potential dips in accuracy. In this study, we put forth an innovative approach, utilizing the CKKS encryption scheme, aiming to harmoniously balance computational efficiency with stringent data privacy. By harnessing the capabilities of Full Homomorphic Encryption (FHE) under the CKKS scheme, we ensure the preservation of privacy, successfully curbing the inherent noise traditionally linked with accuracy reductions in similar encryption-oriented solutions. Through comprehensive experiments, our approach showcased its potential as a strong contender for privacy preservation, demonstrating commendable performance across all tests, affirming that FHE is indeed viable for devices with constrained computational power and energy resources.