The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …

Federated learning (FL) has been considered as a promising paradigm for enabling
distributed training/learning in many machine-learning services without revealing users' …

As data generation increasingly takes place on devices without a wired connection, Machine
Learning (ML) related traffic will be ubiquitous in wireless networks. Many studies have …

Wireless power transfer (WPT) is an emerging paradigm that will enable using wireless to its
full potential in future networks, not only to convey information but also to deliver energy …

Federated edge learning (FEEL) technology for vehicular networks is considered as a
promising technology to reduce the computation workload while kee** the privacy of …

As the rise of information and communication technology, the cooperative work of vehicles
has become crucial in realizing Internet of Vehicles (IoV). Federated learning (FL) is a …

Federated learning (FL) has emerged as a promising alternative to centralized machine
learning for exploiting large amounts of data generated by networks while ensuring data …

Federated learning (FL) is recognized as a promising privacy-preserving distributed
machine learning paradigm, given its potential to enable collaborative model training among …

Remote monitoring systems analyze the environment dynamics in different smart industrial
applications, such as occupational health and safety, and environmental monitoring …

Limited energy resources remain a challenge for wireless devices in federated learning (FL)
networks. To tackle this issue, a joint resource allocation problem is formulated to minimize …