To leverage rich data distributed at the network edge, a new machine-learning paradigm,
called edge learning, has emerged where learning algorithms are deployed at the edge for …

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

Training task in classical machine learning models, such as deep neural networks, is
generally implemented at a remote cloud center for centralized learning, which is typically …

The proliferation of Internet-of-Things (IoT) devices and cloud-computing applications over
siloed data centers is motivating renewed interest in the collaborative training of a shared …

New technological advancements in wireless networks have enlarged the number of
connected devices. The unprecedented surge of data volume in wireless systems …

Efficient collaboration between collaborative machine learning and wireless communication
technology, forming a Federated Edge Learning (FEEL), has spawned a series of next …

Data generated at the network edge can be processed locally by leveraging the paradigm of
edge computing. To fully utilize the widely distributed data, we concentrate on a wireless …

In recent years, wireless federated learning (FL) has been proposed to support the mobile
intelligent applications over the wireless network, which protects the data privacy and …

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 …

To leverage data and computation capabilities of mobile devices, machine learning
algorithms are deployed at the network edge for training artificial intelligence (AI) models …