Future Intelligent Transportation Systems (ITS) can improve on-road safety and
transportation efficiency and vehicular networks (VNs) are essential to enable ITS …

Federated learning (FL) is known to perform machine learning tasks in a distributed manner.
Over the years, this has become an emerging technology, especially with various data …

Federated learning is a revolutionary machine learning approach whose main idea is to
train the machine learning model in a distributed fashion over a large number of edge/end …

Blockchain-enabled Federated Learning (BFL) enables model updates to be stored in
blockchain in a reliable manner. However, one problem is the increase of the training …

Today, the traffic amount is growing inexorably due to the increase in the number of devices
on the network. Researchers analyze traffic by identifying sophisticated dependencies …

Federated Learning provides new opportunities for training machine learning models while
respecting data privacy. This technique is based on heterogeneous devices that work …

Aiming at protecting device data privacy, Federated Learning (FL) is a framework of
distributed machine learning in which devices' local model parameters are exchanged with …

Federated learning (FL) is increasingly considered to circumvent the disclosure of private
data in mobile edge computing (MEC) systems. Training with large data can enhance FL …

In the traditional cloud–edge collaborative Internet of Things (IoT), the high-communication
cost and slow convergence of the models often result in high-delay and energy …

In recent years, various machine learning (ML) solutions have been developed to solve
resource management, interference management, autonomy, and decision-making …