Full leverage of the huge volume of data generated on a large number of user devices for
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …

Pushing artificial intelligence (AI) from central cloud to network edge has reached board
consensus in both industry and academia for materializing the vision of artificial intelligence …

To satisfy the expected plethora of computation-heavy applications, federated edge learning
(FEEL) is a new paradigm featuring distributed learning to carry the capacities of low-latency …

In many remote areas lacking ground communication infrastructure support, such as
wilderness, desert, ocean, etc., an integrated edge computing network in the air with edge …

This paper studies the joint device selection and power control scheme for wireless
federated learning (FL), considering both the downlink and uplink communications between …

Over-the-air federated learning (FL) is a promising privacy-preserving edge artificial
intelligence paradigm, where over-the-air computation enables spectral-efficient model …

To enable wireless federated learning (FL) in communication resource-constrained
networks, two communication schemes, ie, digital and analog ones, are effective solutions …

Over-the-air federated learning (AirFL) allows devices to train a learning model in parallel
and synchronize their local models using over-the-air computation. The integrity of AirFL is …

Due to the dynamics of wireless channels and limited wireless resources (ie, spectrum),
deploying federated learning (FL) over wireless networks is challenged by frequent FL …

Federated edge learning (FL), as an emerging distributed machine learning paradigm,
allows a mass of edge devices to collaboratively train a global model while preserving …