Machine learning (ML) is a widely accepted means for supporting customized services for
mobile devices and applications. Federated Learning (FL), which is a promising approach to …

Federated Learning (FL) has been widely used to train shared machine learning models
while addressing the privacy concerns. When deployed in wireless networks, bandwidth …

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

Owing to the increasing need for massive data analysis and model training at the network
edge, as well as the rising concerns about the data privacy, a new distributed training …

Federated learning (FL) can train a global model from clients' local data set, which can make
full use of the computing resources of clients and performs more extensive and efficient …

Federated learning (FL) is promising in enabling large-scale model training by massive
devices without exposing their local datasets. However, due to limited wireless resources …

The advances of both computation and communication technologies facilitate the
exploitation of massive data generated by mobile devices. It is attractive to leverage these …

Federated learning (FL) can train a global model from clients' local dataset, which can make
full use of the computing resources of clients and performs more extensive and efficient …

Federated Learning (FL) is a promising paradigm for massive data mining service while
protecting users' privacy. In wireless federated learning networks (WFLNs), limited …

There is an increasing interest in a fast-growing machine learning technique called
Federated Learning (FL), in which the model training is distributed over mobile user …