Most conventional Federated Learning (FL) models are using a star network topology where
all users aggregate their local models at a single server (eg, a cloud server). That causes …

Major bottlenecks of large-scale Federated Learning (FL) networks are the high costs for
communication and computation. This is due to the fact that most of current FL frameworks …

Federated learning (FL) is a distributed machine learning technology for next-generation AI
systems that allows a number of workers, ie, edge devices, collaboratively learn a shared …

Federated learning (FL) has recently received considerable attention and is becoming a
popular machine learning (ML) framework that allows clients to train machine learning …

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 …

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 (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 (FL) is a technique for distributed machine learning (ML), in which edge
devices carry out local model training on their individual datasets. In traditional FL …

Increasingly serious data security and privacy protection issues make federated learning
(FL) gradually evolve to be an important technology in the field of artificial intelligence (AI) …

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 …