Artificial Intelligence (AI) at the edge is the utilization of AI in real-world devices. Edge AI
refers to the practice of doing AI computations near the users at the network's edge, instead …

Federated learning (FL) over resource-constrained wireless networks has recently attracted
much attention. However, most existing studies consider one FL task in single-cell wireless …

The safety-critical scenarios of artificial intelligence (AI), such as autonomous driving,
Internet of Things, smart healthcare, etc., have raised critical requirements of trustworthy AI …

River floods are listed among the natural disasters that can directly influence different
aspects of life, ranging from human lives, to economy, infrastructure, agriculture, etc …

Federated learning (FL), as an emerging edge artificial intelligence paradigm, enables many
edge devices to collaboratively train a global model without sharing their private data. To …

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

Edge inference refers to the use of artificial intelligent (AI) models at the network edge to
provide mobile devices inference services and thereby enable intelligent services such as …

In this paper, we consider communication-efficient over-the-air federated learning (FL),
where multiple edge devices with non-independent and identically distributed datasets …

We address the localization of a reconfigurable intelligent surface (RIS) for a single-input
single-output multi-carrier system using bi-static sensing between a fixed transmitter and a …

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 …