The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …

Federated Learning (FL), an emerging paradigm for fast intelligent acquisition at the network
edge, enables joint training of a machine learning model over distributed data sets and …

With the breakthroughs in deep learning and contactless sensors, the recent years have
witnessed a rise of ambient intelligence applications and services, spanning from healthcare …

Edge federated learning (FL) is an emerging paradigm that trains a global parametric model
from distributed datasets based on wireless communications. This paper proposes a unit …

Recently, along with the rapid development of mobile communication technology, edge
computing theory and techniques have been attracting more and more attention from global …

In this paper, we study a relay-assisted federated edge learning (FEEL) network under
latency and bandwidth constraints. In this network, users collaboratively train a global model …

In order to meet the extremely heterogeneous requirements of the next generation wireless
communication networks, research community is increasingly dependent on using machine …

This paper investigates task-oriented communication for edge inference, where a low-end
edge device transmits the extracted feature vector of a local data sample to a powerful edge …

Machine learning and wireless communication technologies are jointly facilitating an
intelligent edge, where federated edge learning (FEEL) is emerging as a promising training …

The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …