Motivated by the increasing computational capacity of wireless user equipments (UEs), eg,
smart phones, tablets, or vehicles, as well as the increasing concerns about sharing private …

The stringent requirements for low-latency and privacy of the emerging high-stake
applications with intelligent devices such as drones and smart vehicles make the cloud …

Recent years have witnessed a rapid proliferation of smart Internet of Things (IoT) devices.
IoT devices with intelligence require the use of effective machine learning paradigms …

With its ability to provide a single platform enabling a variety of services, such as enhanced
mobile broadband communications, virtual reality, automated driving, and the Internet of …

In recent times, we assist to an ever growing diffusion of smart medical sensors and Internet
of things devices that are heavily changing the way healthcare is approached worldwide. In …

Federated learning (FL) allows model training from local data collected by edge/mobile
devices while preserving data privacy, which has wide applicability to image and vision …

Emerging technologies and applications including Internet of Things, social networking, and
crowd-sourcing generate large amounts of data at the network edge. Machine learning …

By leveraging blockchain, this letter proposes a blockchained federated learning (BlockFL)
architecture where local learning model updates are exchanged and verified. This enables …

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 …

We study federated machine learning (ML) at the wireless edge, where power-and
bandwidth-limited wireless devices with local datasets carry out distributed stochastic …