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

Over the past few years, significant advancements have been made in the field of machine
learning (ML) to address resource management, interference management, autonomy, and …

The explosive growth of smart devices (eg, mobile phones, vehicles, drones) with sensing,
communication, and computation capabilities gives rise to an unprecedented amount of …

The widespread deployment of machine learning applications in ubiquitous environments
has sparked interests in exploiting the vast amount of data stored on mobile devices. To …

With increasingly strict data privacy regulations, federated learning (FL) has become one of
the most often heard machine learning techniques due to its privacy-preserving trait. To …

We envision a mobile edge computing (MEC) framework for machine learning (ML)
technologies, which leverages distributed client data and computation resources for training …

Motivated by ever-increasing computational resources at edge devices and increasing
privacy concerns, a new machine learning (ML) framework called federated learning (FL) …

With the rapid development of the Internet of Things (IoT), the need to expand the amount of
data through data-sharing to improve the model performance of edge devices has become …

At the network edge, federated learning (FL) has gained attention as a promising approach
for training deep learning (DL) models collaboratively across a large number of devices …

Federated learning allows mobile devices, ie, workers, to use their local data to
collaboratively train a global model required by the model owner. Federated learning thus …