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 …

Recently, mobile and embedded devices worldwide generate a massive amount of data at
the network edge. To efficiently exploit the data from distributed devices, we concentrate on …

Federated learning (FL) is an effective approach to train models collaboratively among
distributed edge nodes (ie, workers) while facing three crucial challenges, edge …

Federated learning (FL) has recently gained tremendous attention in edge computing and
Internet of Things, due to its capability of enabling distributed clients to cooperatively train …

Device-to-device (D2D)-assisted decentralized learning has been proposed for mobile
devices to collaboratively train artificial intelligence networks without the centralized …

The massive data from mobile and embedded devices have huge potential for training
machine learning models. Decentralized machine learning (DML) can avoid the inherent …

Recently, federated learning (FL) has received intensive research because of its ability in
preserving data privacy for scattered clients to collaboratively train machine learning …

Decentralized learning is widely employed for collaboratively training models using
distributed data over wireless networks. Existing decentralized learning methods primarily …

Abstract Internet-of-Things (IoT) services become closely coupled with machine learning
and cloud computing, where the 5G network provides the connectivity for the IoT devices …

Federated learning (FL) is a key solution for datadriven the Artificial Intelligence of Things
(AIoT). Although much progress has been made, scalability remains a core challenge for …