22 citations · 31 across the 20 of their papers we have counts for
22 papers
ResStreamGS: Residual-Sensitive Dual-Stream Gaussian Splatting for Mobile Volumetric Video Streaming
Haoran Liu, Di Wu, Wang Luo +3
Real-time mobile volumetric video enables immersive free-viewpoint experiences, but delivering high-quality 3D content under constrained bandwidth and computation remains challengi…
Towards Adaptive Federated Graph Clustering: A Global Community-aware Contrastive Learning-based Approach
Yinlin Zhu, Di Wu, Wang Luo +2
Federated graph learning (FGL) enables multiple clients to collaboratively train graph models without sharing their private graph data, providing a promising paradigm for mining kn…
Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity
Yinlin Zhu, Di Wu, Yi Zhang +5
Multimodal-attributed graphs (MAGs), where nodes carry heterogeneous semantic content across multiple modalities while edges encode relational dependencies, have been widely adopte…
RHEA: Reliability-Harmonized Reconstruction and Assignment for Robust Multimodal-Attributed Graph Clustering
Yinlin Zhu, Di Wu, Ziyu Han +4
Multimodal-attributed graphs (MAGs), whose nodes carry heterogeneous attributes such as text and images over a relational structure, have become a fundamental substrate for label-f…
GRM: Utility-Aware Jailbreak Attacks on Audio LLMs via Gradient-Ratio Masking
Yunqiang Wang, Hengyuan Na, Di Wu +2
Audio Large Language Models (ALLMs) enable spoken interaction but introduce new jailbreak vulnerabilities. Existing perturbation-based jailbreaks do not explicitly control which fr…
Adapter-Augmented Bandits for Online Multi-Constrained Multi-Modal Inference Scheduling
Xianzhi Zhang, Yue Xu, Yinlin Zhu +4
Multi-modal large language model (MLLM) inference scheduling enables strong response quality under practical and heterogeneous budgets, beyond what a homogeneous single-backend set…
TMTE: Effective Multimodal Graph Learning with Task-aware Modality and Topology Co-evolution
Yinlin Zhu, Xunkai Li, Di Wu +3
Multimodal-attributed graphs (MAGs) are a fundamental data structure for multimodal graph learning (MGL), enabling both graph-centric and modality-centric tasks. However, our empir…
Both Topology and Text Matter: Revisiting LLM-guided Out-of-Distribution Detection on Text-attributed Graphs
Yinlin Zhu, Di Wu, Xu Wang +2
Text-attributed graphs (TAGs) associate nodes with textual attributes and graph structure, enabling GNNs to jointly model semantic and structural information. Although effective on…
Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach
Yinlin Zhu, Di Wu, Xianzhi Zhang +4
Recent studies of federated graph foundational models (FedGFMs) break the idealized and untenable assumption of having centralized data storage to train graph foundation models, an…
Rethinking Multimodal Point Cloud Completion: A Completion-by-Correction Perspective
Wang Luo, Di Wu, Hengyuan Na +3
Point cloud completion aims to reconstruct complete 3D shapes from partial observations, which is a challenging problem due to severe occlusions and missing geometry. Despite recen…
Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglement
Yinlin Zhu, Xunkai Li, Jishuo Jia +3
Recent advances in graph machine learning have shifted to data-centric paradigms, driven by two emerging fields: (1) Federated graph learning (FGL) enables multi-client collaborati…
BGTplanner: Maximizing Training Accuracy for Differentially Private Federated Recommenders via Strategic Privacy Budget Allocation
Xianzhi Zhang, Yipeng Zhou, Miao Hu +4
To mitigate the rising concern about privacy leakage, the federated recommender (FR) paradigm emerges, in which decentralized clients co-train the recommendation model without expo…
Federated Continual Graph Learning
Yinlin Zhu, Miao Hu, Di Wu
Managing evolving graph data presents substantial challenges in storage and privacy, and training graph neural networks (GNNs) on such data often leads to catastrophic forgetting,…
PPVF: An Efficient Privacy-Preserving Online Video Fetching Framework with Correlated Differential Privacy
Xianzhi Zhang, Yipeng Zhou, Di Wu +3
Online video streaming has evolved into an integral component of the contemporary Internet landscape. Yet, the disclosure of user requests presents formidable privacy challenges. A…
A Survey on Privacy-Preserving Caching at Network Edge: Classification, Solutions, and Challenges
Xianzhi Zhang, Yipeng Zhou, Di Wu +4
Caching content at the edge network is a popular and effective technique widely deployed to alleviate the burden of network backhaul, shorten service delay and improve service qual…
FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning
Yinlin Zhu, Xunkai Li, Zhengyu Wu +3
Subgraph federated learning (subgraph-FL) is a new distributed paradigm that facilitates the collaborative training of graph neural networks (GNNs) by multi-client subgraphs. Unfor…
cRVR: A Stackelberg Game Approach for Joint Privacy-Aware Video Requesting and Edge Caching
Xianzhi Zhang, Linchang Xiao, Yipeng Zhou +4
As users conveniently stream their favorite online videos, video request records are automatically stored by video content providers, which have a high chance of privacy leakage. U…
BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning
Yunchao Yang, Yipeng Zhou, Miao Hu +2
Federated learning (FL) is a prospective distributed machine learning framework that can preserve data privacy. In particular, cross-silo FL can complete model training by making i…
FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment
Jiahao Liu, Jiang Wu, Jinyu Chen +3
Different from conventional federated learning, personalized federated learning (PFL) is able to train a customized model for each individual client according to its unique require…
Edge-Based Video Analytics: A Survey
Miao Hu, Zhenxiao Luo, Amirmohammad Pasdar +3
Edge computing has been getting a momentum with ever-increasing data at the edge of the network. In particular, huge amounts of video data and their real-time processing requiremen…
Virtual Reality: A Survey of Enabling Technologies and its Applications in IoT
Miao Hu, Xianzhuo Luo, Jiawen Chen +3
Virtual Reality (VR) has shown great potential to revolutionize the market by providing users immersive experiences with freedom of movement. Compared to traditional video streamin…
Optimizing Video Caching at the Edge: A Hybrid Multi-Point Process Approach
Xianzhi Zhang, Yipeng Zhou, Di Wu +4
It is always a challenging problem to deliver a huge volume of videos over the Internet. To meet the high bandwidth and stringent playback demand, one feasible solution is to cache…