Edge machine learning involves the deployment of learning algorithms at the network edge
to leverage massive distributed data and computation resources to train artificial intelligence …

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

The increasingly deeper neural networks hinder the democratization of privacy-enhancing
distributed learning, such as federated learning (FL), to resource-constrained devices. To …

Federated Learning (FL) has been proposed as an appealing approach to handle data
privacy issue of mobile devices compared to conventional machine learning at the remote …

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 …

Federated learning enables a cluster of decentralized mobile devices at the edge to
collaboratively train a shared machine learning model, while keeping all the raw training …

In Federated edge learning (FEEL), energy-constrained devices at the network edge
consume significant energy when training and uploading their local machine learning …

This paper studies a federated edge learning system, in which an edge server coordinates a
set of edge devices to train a shared machine learning (ML) model based on their locally …

To satisfy the expected plethora of computation-heavy applications, federated edge learning
(FEEL) is a new paradigm featuring distributed learning to carry the capacities of low-latency …

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 …