Personalized Federated Learning (PFL) is a new Federated Learning (FL) paradigm,
particularly tackling the heterogeneity issues brought by various mobile user equipments …

Machine learning (ML) is powering a rapidly-increasing number of web applications. As a
crucial part of 5G, edge computing facilitates edge artificial intelligence (AI) by ML model …

Federated learning (FL) has been widely adopted to train machine learning models over
massive data in edge computing. However, machine learning faces critical challenges, eg …

Emerging technologies and applications including Internet of Things (IoT), social
networking, and crowd-sourcing generate large amounts of data at the network edge …

By placing the computing, storage and networking resources close to the end users,
distributed edge computing greatly benefits the performance of 5G communication systems …

Deep neural networks (DNNs) have become a critical component for inference in modern
mobile applications, but the efficient provisioning of DNNs is non-trivial. Existing mobile-and …

The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …

When applying machine learning techniques to the Internet of things, aggregating massive
amount of data seriously reduce the system efficiency. To tackle this challenge, a distributed …

Artificial intelligence (AI) has achieved remarkable breakthroughs in a wide range of fields,
ranging from speech processing, image classification to drug discovery. This is driven by the …

The accelerating convergence of artificial intelligence and edge computing has sparked a
recent wave of interest in edge intelligence. While pilot efforts focused on edge DNN …