Image encryption is a critical component of modern data security, ensuring the
confidentiality, integrity, and privacy of sensitive visual content. In this paper, we present a …

Federated learning is an emerging technology that enables the decentralised training of
machine learning-based methods for medical image analysis across multiple sites while …

This study presents a novel privacy-preserving self-supervised (SSL) framework for COVID-
19 classification from lung CT scans, utilizing federated learning (FL) enhanced with Paillier …

Federated learning (FL) is considered a de facto standard for privacy preservation in AI
environments because it does not require data to be aggregated in some central place to …

Federated Learning (FL) is a technique that can learn a global machine-learning model at a
central server by aggregating locally trained models. This distributed machine-learning …

Federated learning (FL) allows multiple participants to collaborate to train a machine
learning model while ensuring that the data remain local. This approach has seen extensive …

This special issue is a collection of emerging trends and challenges in applying learning-
driven approaches to data fabric architectures within the cloud-to-thing continuum. As data …

In an era where collaborative data analysis and privacy protection are paramount, federated
learning emerges as a transformative paradigm. This paper delves into integrating …

As data becomes the cornerstone of digital ecosystems, securing databases against
evolving cyber threats is imperative. This paper explores the critical role of encryption in …

Federated learning avoids centralizing data in a central server by distributing the model
training process across devices, thus protecting privacy to some extent. However, existing …