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 …

Decentralized Federated Learning (DeFL) plays a critical role in improving effectiveness of
training and has been proved to give great scope to the development of edge computing …

The emergence of new services and applications in emerging wireless networks (eg,
beyond 5G and 6G) has shown a growing demand for the usage of artificial intelligence (AI) …

We consider distributed machine learning at the wireless edge, where a parameter server
builds a global model with the help of multiple wireless edge devices that perform …

Wireless connectivity is instrumental in enabling scalable federated learning (FL), yet
wireless channels bring challenges for model training, in which channel randomness …

Distributed machine learning has been extensively employed in wireless systems, which
can leverage abundant data distributed over massive devices to collaboratively train a high …

Federated learning includes mobile phones for cooperative learning and training and
contains limited data on device. Federated learning allocates the machine learning …

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

In this paper, the problem of federated learning (FL) through digital communication between
clients and a parameter server (PS) over a multiple access channel (MAC), also subject to …

Federated learning (FL) is a swiftly evolving field within machine learning for collaboratively
training models at the network edge in a privacy-preserving fashion, without training data …