31 citations · 71 across the 26 of their papers we have counts for
12 papers · 1 filter
Towards Heterogeneity-Aware and Energy-Efficient Topology Optimization for Decentralized Federated Learning in Edge Environment
Yuze Liu, Tiehua Zhang, Zhishu Shen +3
Federated learning (FL) has emerged as a promising paradigm within edge computing (EC) systems, enabling numerous edge devices to collaboratively train artificial intelligence (AI)…
MetaSTH-Sleep: Towards Effective Few-Shot Sleep Stage Classification for Health Management with Spatial-Temporal Hypergraph Enhanced Meta-Learning
Jingyu Li, Tiehua Zhang, Jinze Wang +6
Accurate classification of sleep stages based on bio-signals is fundamental not only for automatic sleep stage annotation, but also for clinical health management and continuous sl…
Leveraging Auxiliary Task Relevance for Enhanced Bearing Fault Diagnosis through Curriculum Meta-learning
Jinze Wang, Jiong Jin, Tiehua Zhang +3
The accurate diagnosis of machine breakdowns is crucial for maintaining operational safety in smart manufacturing. Despite the promise shown by deep learning in automating fault id…
Towards Secure and Efficient Data Scheduling for Vehicular Social Networks
Youhua Xia, Tiehua Zhang, Jiong Jin +2
Efficient data transmission scheduling within vehicular environments poses a significant challenge due to the high mobility of such networks. Contemporary research predominantly ce…
CHASE: A Causal Hypergraph based Framework for Root Cause Analysis in Multimodal Microservice Systems
Ziming Zhao, Zhenwei Wang, Tiehua Zhang +7
In recent years, the widespread adoption of distributed microservice architectures within the industry has significantly increased the demand for enhanced system availability and r…
Exploiting Spatial-temporal Data for Sleep Stage Classification via Hypergraph Learning
Yuze Liu, Ziming Zhao, Tiehua Zhang +5
Sleep stage classification is crucial for detecting patients' health conditions. Existing models, which mainly use Convolutional Neural Networks (CNN) for modelling Euclidean data…
Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs
Tiehua Zhang, Yuze Liu, Zhishu Shen +4
Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learni…
Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning Networks
Tiehua Zhang, Yuze Liu, Yao Yao +4
Heterogeneous graph neural network has unleashed great potential on graph representation learning and shown superior performance on downstream tasks such as node classification and…
An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning
Tiehua Zhang, Yuze Liu, Zhishu Shen +4
Spatial-temporal data contains rich information and has been widely studied in recent years due to the rapid development of relevant applications in many fields. For instance, medi…
GPS: A Policy-driven Sampling Approach for Graph Representation Learning
Tiehua Zhang, Yuze Liu, Xin Chen +3
Graph representation learning has drawn increasing attention in recent years, especially for learning the low dimensional embedding at both node and graph level for classification…
STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks
Guannan Lou, Yuze Liu, Tiehua Zhang +1
We present a spatial-temporal federated learning framework for graph neural networks, namely STFL. The framework explores the underlying correlation of the input spatial-temporal d…
Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses
Yao Deng, Tiehua Zhang, Guannan Lou +3
The rapid development of artificial intelligence, especially deep learning technology, has advanced autonomous driving systems (ADSs) by providing precise control decisions to coun…