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

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 …

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

Machine learning and wireless communication technologies are jointly facilitating an
intelligent edge, where federated edge learning (FEEL) is emerging as a promising training …

We envision a mobile edge computing (MEC) framework for machine learning (ML)
technologies, which leverages distributed client data and computation resources for training …

Federated edge learning (FEEL) is a popular distributed learning framework that allows
privacy-preserving collaborative model training via periodic learning-updates …

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 …

Owing to the increasing need for massive data analysis and model training at the network
edge, as well as the rising concerns about the data privacy, a new distributed training …

There is an increasing interest in a new machine learning technique called Federated
Learning, in which the model training is distributed over mobile user equipments (UEs), and …

With the proliferation of Internet of Things (IoT), zillions of bytes of data are generated at the
network edge, incurring an urgent need to push the frontiers of artificial intelligence (AI) to …