In recent years, the way that machine learning is used has undergone a paradigm shift
driven by distributed and collaborative learning. Several approaches have emerged to …

With the rapid growth in the volume of data sets, models, and devices in the domain of deep
learning, there is increasing attention on large-scale distributed deep learning. In contrast to …

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 …

To keep pace with the explosive growth of Artificial Intelligence (AI) and Machine Learning
(ML)-dominated applications, distributed intelligence solutions are gaining momentum …

Given the frequently changing and potentially unreliable environment, the seamless
handover authentication is essential to achieve zero-trust Internet of Vehicles (IoV) network …

Recent advances in decentralized deep learning algorithms have demonstrated cutting-
edge performance on various tasks with large pre-trained models. However a pivotal …

We consider a distributed learning problem, where agents minimize a global objective
function by exchanging information over a network. Our approach has two distinct …

The metaverse is expected to provide immersive entertainment, education, and business
applications. However, virtual reality (VR) transmission over wireless networks is data-and …

Although edge intelligence (EI) propels the development of Internet of Things (IoT)
applications to a new stage, the distributed nature of the end users in EI networks greatly …

Developing efficient solutions for inference problems in intelligent sensor networks is crucial
for the next generation of location, tracking, and mapping services. This paper develops a …