The conventional federated learning (FedL) architecture distributes machine learning (ML)
across worker devices by having them train local models that are periodically aggregated by …

Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging
technological framework focusing on seamless integration of AI, communication networks …

Next-generation wireless networks must support ultra-reliable, low-latency communication
and intelligently manage a massive number of Internet of Things (IoT) devices in real-time …

Advances in artificial intelligence (AI) and wireless technology are driving forward the large
deployment of interconnected smart technologies that constitute cyber–physical systems …

Edge machine learning can deliver low-latency and private artificial intelligent (AI) services
for mobile devices by leveraging computation and storage resources at the network edge …

Federated Learning (FL) is a distributed machine learning technique, where each device
contributes to the learning model by independently computing the gradient based on its …

Over-the-air federated edge learning (Air-FEEL) has emerged as a communication-efficient
solution to enable distributed machine learning over edge devices by using their data locally …

Federated learning becomes increasingly attractive in the areas of wireless communications
and machine learning due to its powerful learning ability and potential applications. In …

The aim of this paper is to propose a resource allocation strategy for dynamic training and
inference of machine learning tasks at the edge of the wireless network, with the goal of …

We study federated learning (FL) at the wireless edge, where power-limited devices with
local datasets collaboratively train a joint model with the help of a remote parameter server …