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 …

Federated learning is a distributed machine learning technology that can protect users' data
privacy, so it has attracted more and more attention in the industry and academia …

Federated learning (FL) is a distributed learning paradigm that enables a large number of
mobile devices to collaboratively learn a model under the coordination of a central server …

Federated learning provides a promising paradigm to enable network edge intelligence in
the future sixth generation (6G) systems. However, due to the high dynamics of wireless …

Federated learning (FL) has recently emerged as an important and promising learning
scheme in IoT, enabling devices to jointly learn a model without sharing their raw data sets …

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) is a powerful distributed machine learning framework where a
server aggregates models trained by different clients without accessing their private data …

Federated learning (FL) has recently become one of the hottest focuses in wireless edge
networks with the ever-increasing computing capability of user equipment (UE). In FL, UEs …

Federated learning (FL) has been widely used to train machine learning models over
massive data in edge computing. However, the existing FL solutions may cause long …

In recent years, wireless federated learning (FL) has been proposed to support the mobile
intelligent applications over the wireless network, which protects the data privacy and …