134 citations · 232 across the 14 of their papers we have counts for
7 papers · 1 filter
Enhancing Federated Learning with spectrum allocation optimization and device selection
Tinghao Zhang, Kwok-Yan Lam, Jun Zhao +3
Machine learning (ML) is a widely accepted means for supporting customized services for mobile devices and applications. Federated Learning (FL), which is a promising approach to i…
Mobile Augmented Reality with Federated Learning in the Metaverse
Xinyu Zhou, Jun Zhao
The Metaverse is deemed the next evolution of the Internet and has received much attention recently. Metaverse applications via mobile augmented reality (MAR) require rapid and acc…
Edge-Cloud Cooperation for DNN Inference via Reinforcement Learning and Supervised Learning
Tinghao Zhang, Zhijun Li, Yongrui Chen +2
Deep Neural Networks (DNNs) have been widely applied in Internet of Things (IoT) systems for various tasks such as image classification and object detection. However, heavyweight D…
Facial Landmark Predictions with Applications to Metaverse
Qiao Han, Jun Zhao, Kwok-Yan Lam
This research aims to make metaverse characters more realistic by adding lip animations learnt from videos in the wild. To achieve this, our approach is to extend Tacotron 2 text-t…
Resource Allocation and Resolution Control in the Metaverse with Mobile Augmented Reality
Peiyuan Si, Jun Zhao, Huimei Han +2
With the development of blockchain and communication techniques, the Metaverse is considered as a promising next-generation Internet paradigm, which enables the connection between…
Efficient Dropout-resilient Aggregation for Privacy-preserving Machine Learning
Ziyao Liu, Jiale Guo, Kwok-Yan Lam +1
With the increasing adoption of data-hungry machine learning algorithms, personal data privacy has emerged as one of the key concerns that could hinder the success of digital trans…
Privacy-Preserving Aggregation in Federated Learning: A Survey
Ziyao Liu, Jiale Guo, Wenzhuo Yang +3
Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms and growing concerns over personal data privacy, Privacy-Preserving Federated Learning (PP…