Emerging technologies and applications including Internet of Things, social networking, and
crowd-sourcing generate large amounts of data at the network edge. Machine learning …

Federated learning (FL) has been widely adopted to train machine learning models over
massive data in edge computing. However, machine learning faces critical challenges, eg …

Emerging technologies and applications including Internet of Things (IoT), social
networking, and crowd-sourcing generate large amounts of data at the network edge …

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

Federated learning (FL) is a distributed learning paradigm that enables a large number of
mobile devices to collaboratively learn a model under the coordination of a central server …

Data generated at the network edge can be processed locally by leveraging the paradigm of
Edge Computing (EC). Aided by EC, Federated Learning (FL) has been becoming a …

The emerging Federated Learning (FL) enables IoT devices to collaboratively learn a
shared model based on their local datasets. However, due to end devices' heterogeneity, it …

Distributed learning algorithms aim to leverage distributed and diverse data stored at users'
devices to learn a global phenomena by performing training amongst participating devices …

The combination of artificial intelligence and mobile edge computing (MEC) is considered as
a promising evolution path of the future wireless networks. As a model-level coordination …

Data generated at the network edge can be processed locally by leveraging the paradigm of
edge computing (EC). Aided by EC, decentralized federated learning (DFL), which …