Split learning (SL) is a collaborative learning framework, which can train an artificial
intelligence (AI) model between a device and an edge server by splitting the AI model into a …

Federated learning (FL) is a popular framework for training an AI model using distributed
mobile data in a wireless network. It features data parallelism by distributing the learning …

Wider coverage and a better solution to a latency reduction in 5G necessitate its
combination with multi-access edge computing technology. Decentralized deep learning …

Edge machine learning involves the development of learning algorithms at the network edge
to leverage massive distributed data and computation resources. Among others, the …

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

Federated learning (FL) is a newly emerged branch of AI that facilitates edge devices to
collaboratively train a global machine learning model without centralizing data and with …

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 …

Distributed learning systems are increasingly being adopted for a variety of applications as
centralized training becomes unfeasible. A few architectures have emerged to divide and …

Emerging mobile edge techniques and applications such as Augmented Reality (AR)/Virtual
Reality (VR), Internet of Things (IoT), and vehicle networking, result in an explosive growth of …

Federated learning (FL) is an attractive distributed learning paradigm supporting real-time
continuous learning and client privacy by default. In most FL approaches, all edge clients …