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 …

Federated learning (FL) involves training machine learning models over distributed edge
nodes (ie, workers) while facing three critical challenges, edge heterogeneity, Non-IID data …

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 distributed data sources in edge computing. However, the existing FL frameworks …

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 …

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 …

Federated learning (FL) has emerged in edge computing to address limited bandwidth and
privacy concerns of traditional cloud-based centralized training. However, the existing FL …

When applying machine learning techniques to the Internet of things, aggregating massive
amount of data seriously reduce the system efficiency. To tackle this challenge, a distributed …

In edge computing (EC), federated learning (FL) enables massive devices to collaboratively
train AI models without exposing local data. In order to avoid the possible bottleneck of the …

Federated Learning (FL) is an emerging approach in edge computing for collaboratively
training machine learning models among multiple devices, which aims to address limited …