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.

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.

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.