The Metaverse has received much attention recently. Metaverse applications via mobile
augmented reality (MAR) require rapid and accurate object detection to mix digital data with …

Federated learning is promising in enabling large-scale machine learning by massive
mobile devices without exposing the raw data of users with strong privacy concerns. Existing …

Mobile edge computing (MEC) has been considered as a promising technology to provide
seamless integration of multiple application services. Federated learning (FL) is carried out …

As the large-scale deployment of machine learning applications, there is much research
attention on exploiting a vast amount of data stored on mobile clients. To preserve data …

Metaverse is envisioned to merge the actual world with a virtual world to bring users
unprecedented immersive feelings. To ensure user experience, federated learning (FL) has …

This paper studies the problem of training federated deep learning models over a mobile
environment. Stemming from the federated learning (FL) concept, deep learning models on …

Federated learning is a new paradigm to support resource-intensive and privacy-aware
learning applications. It enables the Internet-of-Things (IoT) devices to collaboratively train a …

Federated learning has emerged as a promising, massively distributed way to train a joint
deep model over large amounts of edgedevices while keeping private user data strictly on …

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

The past decade has witnessed the prosperous growth of augmented reality (AR) devices,
as they provide immersive and interactive experience for customers. AR applications have …