Recently, Internet of Things (IoT) systems in the network edge with embedded intelligence
emerged as a trending research topic. Edge computing offers a significant advantage over …

The proliferation of the Internet of Things (IoT) and widespread use of devices with sensing,
computing, and communication capabilities have motivated intelligent applications …

Federated learning (FL) is a new machine learning framework that trains a joint model
across a large number of decentralized computing devices. Existing methods, eg, Federated …

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 …

Federated learning obtains a central model on the server by aggregating models trained
locally on clients. As a result, federated learning does not require clients to upload their data …

Federated learning is an emerging concept that trains the machine learning models with the
local distributed data sets, without sending the raw data to the data center. But, in the …

In this work, we investigate the challenging problem of on-demand federated learning (FL)
over heterogeneous edge devices with diverse resource constraints. We propose a cost …

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 …

The convergence of the Internet of Things (IoT) and data analytics has great potential to
accelerate knowledge discovery, while the traditional approach of centralized data collection …

Internet of Things (IoT) have widely penetrated in different aspects of modern life and many
intelligent IoT services and applications are emerging. Recently, federated learning is …