Communication limitation at the edge is widely recognized as a major bottleneck for
federated learning (FL). Multi-hop wireless networking provides a cost-effective solution to …

Federated learning is a promising technique to implement network intelligence for the sixth
generation (6G) communication systems. However, the collected data in wireless networks …

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 …

The conventional federated learning (FL) framework usually assumes synchronous
reception and fusion of all the local models at the central aggregator and synchronous …

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) 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 …

In this article, we study device selection and resource allocation (DSRA) for layerwise
federated learning (FL) in wireless networks. For effective learning, DSRA should be …

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 …

In this paper, the convergence time of federated learning (FL), when deployed over a
realistic wireless network, is studied. In particular, a wireless network is considered in which …

A remarkable interest in machine learning-based schemes as key enablers for
nextgeneration intelligent wireless systems has been observed. Most of the existing learning …