Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

Federated Learning is a framework that coordinates a large amount of workers to train a
shared model in a distributed manner, in which the training data are located on the workers' …

To unveil the hidden value in the datasets of user equipments (UEs) while preserving user
privacy, federated learning (FL) is emerging as a promising technique to train a machine …

Leveraging various mobile devices to train the shared model collaboratively, federated
learning (FL) can improve the privacy and security of 6G communication. To economically …

Federated learning (FL) enables multiple devices to collaboratively train a shared model
while preserving data privacy. However, despite its emerging applications in many areas …

Mobile users are often reluctant to participate in federated learning to train models, due to
the excessive consumption of the limited resources such as the mobile devices' energy. We …

The emerging federated optimization paradigm performs data mining or artificial intelligence
techniques locally on the edge devices, enabling scientists and engineers to utilize the …

With the booming development of fifth-generation network technology and Internet of Things,
the number of end-user devices (EDs) and diverse applications is surging, resulting in …

Federated learning (FL) has recently received considerable attention in Internet of Things,
due to its capability of letting multiple clients collaboratively train machine learning models …

Federated Learning is designed for multiple mobile devices to collaboratively train an
artificial intelligence model while preserving data privacy. Instead of collecting the raw …