Federated Learning (FL) is a concept first introduced by Google in 2016, in which multiple
devices collaboratively learn a machine learning model without sharing their private data …

Federated learning (FL) is a promising framework for distributed machine learning that trains
models without sharing local data while protecting privacy. FL exploits the concept of …

The vision of the upcoming 6G technologies that have fast data rate, low latency, and ultra-
dense network, draws great attentions to the Internet of Vehicles (IoV) and Vehicle-to …

As a distributed deep learning paradigm, federated learning (FL) provides a powerful tool for
the accurate and efficient processing of on-board data in vehicular edge computing (VEC) …

The last two decades have seen a clear trend toward crafting intelligent vehicles based on
the significant advances in communication and computing paradigms, which provide a safer …

Machine learning (ML) has succeeded in improving our daily routines by enabling
automation and improved decision making in a variety of industries such as healthcare …

Federated learning (FL) is a promising decentralized deep learning technology, which
allows users to update models cooperatively without sharing their data. FL is reshaping …

Message exchange among vehicles plays an important role in ensuring road safety.
Emergency message dissemination is usually carried out by broadcasting. However, high …

With the advent of the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning
(ML), and Deep Learning (DL) algorithms, the landscape of data-driven medical applications …

Computer networks are dealing with growing complexity, given the ever-increasing volume
of data produced by all sorts of network nodes. Performance improvements are a non-stop …