Unmanned Aerial Vehicles (UAVs) are increasingly being used in a high-computation
paradigm enabled with smart applications in the Beyond Fifth Generation (B5G) wireless …

Modern healthcare is transforming from hospital-centric to individual-centric systems.
Emerging implantable and wearable medical (IWM) devices are integral parts of enabling …

Federated learning (FL) is a popular edge learning approach that utilizes local data and
computing resources of network edge devices to train machine learning (ML) models while …

Since the invention in 2016, federated learning (FL) has been a key concept of artificial
intelligence, in which the data of FL users needs not to be uploaded to the central server …

The massive number of Internet of Things (IoT) devices connected to the Internet is
continuously increasing. The operations of these devices rely on consuming huge amounts …

Artificial intelligence (AI) techniques implemented at a large scale in intelligent transport
systems (ITS), have considerably enhanced the vehicles' autonomous behaviour in making …

As data generation increasingly takes place on devices without a wired connection, Machine
Learning (ML) related traffic will be ubiquitous in wireless networks. Many studies have …

This paper proposes a vehicular edge federated learning (VEFL) solution, where an edge
server leverages highly mobile connected vehicles'(CVs') onboard central processing units …

Federated learning (FL), an emerging distributed machine learning (ML) technique, allows
massive embedded devices and a server to work together for training a global ML model …

Motivated by ever-increasing computational resources at edge devices and increasing
privacy concerns, a new machine learning (ML) framework called federated learning (FL) …