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 …

Federated learning is a privacy-preserving machine learning technique that trains models
across multiple devices holding local data samples without exchanging them. There are …

Recently, innovations in the Internet of Medical Things (IoMT), information and
communication technologies, and machine learning (ML) have enabled smart healthcare …

Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …

Abstract Social Internet of Things (SIoT) with deep integration of Internet of Things and social
networks has become a target of a large number of hackers who attempt to spread viruses …

Abstract Internet of Things (IoT) has been widely used in many smart applications such as
smart cities, smart agriculture, healthcare, industry, etc. In addition, the importance of IoT …

Full leverage of the huge volume of data generated on a large number of user devices for
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …

The Internet of Vehicles is the specific instantiation of the Internet of Things in the field of
transportation. Vehicle and driving data are often used to mine information about people's …

In recent years, federated learning (FL) has become a very popular paradigm for training
distributed, large-scale, and privacy-preserving machine learning (ML) systems. In contrast …

The unexpected and rapid spread of the COVID-19 pandemic has amplified the acceptance
of remote healthcare systems such as telemedicine. Telemedicine effectively provides …