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 emerged as a significant advancement in the field of Artificial
Intelligence (AI), enabling collaborative model training across distributed devices while …

Federated learning enables data owners to jointly train a neural network without sharing
their personal data, which makes it possible to share sensitive data generated from various …

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 …

Federated learning (FL) has achieved state-of-the-art performance in distributed learning
tasks with privacy requirements. However, it has been discovered that FL is vulnerable to …

Federated learning has gained extensive interest in recent years owing to its ability to
update model parameters without obtaining raw data from users, which makes it a viable …

The classification of remote sensing images can give valuable data for various practical
applications for smart cities, including urban planning, construction, and water resource …

Decentralized federated learning integrates advanced technologies, including distributed
computing and secure encryption methodologies, to facilitate a robust and efficient …

Federated learning (FL) provides convenience for cross-domain machine learning
applications and has been widely studied. However, the original FL is still vulnerable to …

In vehicular ad-hoc networks (VANET), federated learning enables vehicles to
collaboratively train a global model for intelligent transportation without sharing their local …