In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

Recent advances in electronic devices and communication infrastructure have
revolutionized the traditional healthcare system into a smart healthcare system by using …

Over the past five years, interest in the literature regarding the security of the Internet of
Medical Things (IoMT) has increased. Due to the enhanced interconnectedness of IoMT …

Advances in the new generation of Internet of Things (IoT) technology are propelling the
growth of intelligent industrial applications worldwide. Simultaneously, widespread adoption …

With the increasing penetration of the Internet of things (IoT) into people's lives, the
limitations of traditional medical systems are emerging. First, the typical way of handling …

Federated learning (FL) is a promising technology for achieving privacy-preserving edge
intelligence and has attracted extensive attention from industry and academia. However, in …

In the realm of federated learning (FL), the conventional dual-layered architecture,
comprising a central parameter server and peripheral devices, often encounters challenges …

Recent advancements in artificial intelligence (AI) and IoT technology have revolutionized
the healthcare industry by providing effective remote healthcare. Furthermore, with the aging …

In the current field of disease risk prediction research, there are many methods of using
servers for centralized computing to train and infer prediction models. However, this …

Federated Learning (FL) enables the distributed machine learning (ML) without violating the
privacy of local users. In the scenario wireless FL, it is challenging for some local clients to …