In distributed optimization and machine learning, multiple nodes coordinate to solve large
problems. To do this, the nodes need to compress important algorithm information to bits so …

As the 5G communication networks are being widely deployed worldwide, both industry and
academia have started to move beyond 5G and explore 6G communications. It is generally …

Decentralized stochastic gradient descent (SGD) is a driving engine for decentralized
federated learning (DFL). The performance of decentralized SGD is jointly influenced by …

Federated learning (FL) is an efficient and privacy-preserving distributed learning paradigm
that enables massive edge devices to train machine learning models collaboratively …

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

The deployment of federated learning in a wireless network, called federated edge learning
(FEEL), exploits low-latency access to distributed mobile data to efficiently train an AI model …

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

There is an increasing interest in a new machine learning technique called Federated
Learning, in which the model training is distributed over mobile user equipments (UEs), and …

Federated learning (FL) as a promising edge-learning framework can effectively address the
latency and privacy issues by featuring distributed learning at the devices and model …

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 …