Intrusion Detection Systems (IDS) are essential for securing computer networks by
identifying and mitigating potential threats. However, traditional IDS face challenges related …

Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Intelligent transportation systems (ITSs) have been fueled by the rapid development of
communication technologies, sensor technologies, and the Internet of Things (IoT) …

In recent years, as the boundaries of computing have expanded with the emergence of the
Internet of Things (IoT) and its increasing number of devices continuously producing flows of …

Abstract While Federated Learning (FL) provides a privacy-preserving approach to analyze
sensitive data without centralizing training data, the field lacks an detailed comparison of …

Federated learning (FL) is a distributed machine learning process, which allows multiple
nodes to work together to train a shared model without exchanging raw data. It offers several …

The amount of biomedical data continues to grow rapidly. However, collecting data from
multiple sites for joint analysis remains challenging due to security, privacy, and regulatory …

The evolution of cybersecurity is undoubtedly associated and intertwined with the
development and improvement of artificial intelligence (AI). As a key tool for realizing more …

Federated Learning (FL) has emerged as a promising distributed learning paradigm with an
added advantage of data privacy. With the growing interest in having collaboration among …

Federated learning (FL) has been an area of active research in recent years. There have
been numerous studies in FL to make it more successful in the presence of data …