74 citations · 112 across the 9 of their papers we have counts for
9 papers
Causal-Aware Intelligent QoE Optimization for VR Interaction with Adaptive Keyframe Extraction
Ziru Zhang, Jiadong Yu, Danny H. K. Tsang
The optimization of quality of experience (QoE) in multi-user virtual reality (VR) interactions demands a delicate balance between ultra-low latency, high-fidelity motion synchroni…
Learning Design-Score Manifold to Guide Diffusion Models for Offline Optimization
Tailin Zhou, Zhilin Chen, Wenlong Lyu +3
Optimizing complex systems, from discovering therapeutic drugs to designing high-performance materials, remains a fundamental challenge across science and engineering, as the under…
Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing System
Tailin Zhou, Jiadong Yu, Jun Zhang +1
This paper investigates resource allocation to provide heterogeneous users with customized virtual reality (VR) services in a mobile edge computing (MEC) system. We first introduce…
Mode Connectivity and Data Heterogeneity of Federated Learning
Tailin Zhou, Jun Zhang, Danny H. K. Tsang
Federated learning (FL) enables multiple clients to train a model while keeping their data private collaboratively. Previous studies have shown that data heterogeneity between clie…
Attention-based QoE-aware Digital Twin Empowered Edge Computing for Immersive Virtual Reality
Jiadong Yu, Ahmad Alhilal, Tailin Zhou +2
Metaverse applications such as virtual reality (VR) content streaming, require optimal resource allocation strategies for mobile edge computing (MEC) to ensure a high-quality user…
Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Data
Tailin Zhou, Zehong Lin, Jun Zhang +1
Model averaging is a widely adopted technique in federated learning (FL) that aggregates multiple client models to obtain a global model. Remarkably, model averaging in FL yields a…
FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Data
Tailin Zhou, Jun Zhang, Danny H. K. Tsang
Federated learning allows multiple clients to collaboratively train a model without exchanging their data, thus preserving data privacy. Unfortunately, it suffers significant perfo…
Bi-directional Digital Twin and Edge Computing in the Metaverse
Jiadong Yu, Ahmad Alhilal, Pan Hui +1
The Metaverse has emerged to extend our lifestyle beyond physical limitations. As essential components in the Metaverse, digital twins (DTs) are the real-time digital replicas of p…
A Benchmarking Framework for Interactive 3D Applications in the Cloud
Tianyi Liu, Sen He, Sunzhou Huang +4
With the growing popularity of cloud gaming and cloud virtual reality (VR), interactive 3D applications have become a major type of workloads for the cloud. However, despite their…