5 citations · 14 across the 13 of their papers we have counts for
13 papers
PrivTuner with Homomorphic Encryption and LoRA: A P3EFT Scheme for Privacy-Preserving Parameter-Efficient Fine-Tuning of AI Foundation Models
Yang Li, Wenhan Yu, Jun Zhao
AI foundation models have recently demonstrated impressive capabilities across a wide range of tasks. Fine-tuning (FT) is a method of customizing a pre-trained AI foundation model…
Orchestration of Emulator Assisted Mobile Edge Tuning for AI Foundation Models: A Multi-Agent Deep Reinforcement Learning Approach
Wenhan Yu, Terence Jie Chua, Jun Zhao
The efficient deployment and fine-tuning of foundation models are pivotal in contemporary artificial intelligence. In this study, we present a groundbreaking paradigm integrating M…
FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing
Terence Jie Chua, Wenhan Yu, Jun Zhao +1
The emergence of foundation models, including language and vision models, has reshaped AI's landscape, offering capabilities across various applications. Deploying and fine-tuning…
Heterogeneous 360 Degree Videos in Metaverse: Differentiated Reinforcement Learning Approaches
Wenhan Yu, Jun Zhao
Advanced video technologies are driving the development of the futuristic Metaverse, which aims to connect users from anywhere and anytime. As such, the use cases for users will be…
Semantic communications, semantic edge computing, and semantic caching
Wenhan Yu, Jun Zhao
The increasing popularity of applications like the Metaverse has led to the exploration of new, more effective ways of communication. Semantic communication, which focuses on the m…
Human-Centric Resource Allocation in the Metaverse over Wireless Communications
Jun Zhao, Liangxin Qian, Wenhan Yu
The Metaverse will provide numerous immersive applications for human users, by consolidating technologies like extended reality (XR), video streaming, and cellular networks. Optimi…
Detection of Uncertainty in Exceedance of Threshold (DUET): An Adversarial Patch Localizer
Terence Jie Chua, Wenhan Yu, Jun Zhao
Development of defenses against physical world attacks such as adversarial patches is gaining traction within the research community. We contribute to the field of adversarial patc…
Virtual Reality in Metaverse over Wireless Networks with User-centered Deep Reinforcement Learning
Wenhan Yu, Terence Jie Chua, Jun Zhao
The Metaverse and its promises are fast becoming reality as maturing technologies are empowering the different facets. One of the highlights of the Metaverse is that it offers the…
Play to Earn in the Metaverse with Mobile Edge Computing over Wireless Networks: A Deep Reinforcement Learning Approach
Terence Jie Chua, Wenhan Yu, Jun Zhao
The Metaverse play-to-earn games have been gaining popularity as they enable players to earn in-game tokens which can be translated to real-world profits. With the advancements in…
User-centric Heterogeneous-action Deep Reinforcement Learning for Virtual Reality in the Metaverse over Wireless Networks
Wenhan Yu, Terence Jie Chua, Jun Zhao
The Metaverse is emerging as maturing technologies are empowering the different facets. Virtual Reality (VR) technologies serve as the backbone of the virtual universe within the M…
Mobile Edge Computing for the Metaverse
Chang Liu, Yitong Wang, Jun Zhao
The Metaverse has emerged as the next generation of the Internet. It aims to provide an immersive, persistent virtual space where people can live, learn, work and interact with eac…
Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality
Xinyu Zhou, Chang Liu, Jun Zhao
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 t…
Time Minimization in Hierarchical Federated Learning
Chang Liu, Terence Jie Chua, Jun Zhao
Federated Learning is a modern decentralized machine learning technique where user equipments perform machine learning tasks locally and then upload the model parameters to a centr…