24 citations · 64 across the 13 of their papers we have counts for
13 papers
UniTrans: A Unified Vertical Federated Knowledge Transfer Framework for Enhancing Cross-Hospital Collaboration
Chung-ju Huang, Yuanpeng He, Xiao Han +3
Cross-hospital collaboration has the potential to address disparities in medical resources across different regions. However, strict privacy regulations prohibit the direct sharing…
Graph Contrastive Learning with Cohesive Subgraph Awareness
Yucheng Wu, Leye Wang, Xiao Han +1
Graph contrastive learning (GCL) has emerged as a state-of-the-art strategy for learning representations of diverse graphs including social and biomedical networks. GCL widely uses…
Safety and Performance, Why Not Both? Bi-Objective Optimized Model Compression against Heterogeneous Attacks Toward AI Software Deployment
Jie Zhu, Leye Wang, Xiao Han +2
The size of deep learning models in artificial intelligence (AI) software is increasing rapidly, hindering the large-scale deployment on resource-restricted devices (e.g., smartpho…
Cross-center Early Sepsis Recognition by Medical Knowledge Guided Collaborative Learning for Data-scarce Hospitals
Ruiqing Ding, Fangjie Rong, Xiao Han +1
There are significant regional inequities in health resources around the world. It has become one of the most focused topics to improve health services for data-scarce hospitals an…
Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks
Chung-ju Huang, Leye Wang, Xiao Han
Collaboration between healthcare institutions can significantly lessen the imbalance in medical resources across various geographic areas. However, directly sharing diagnostic info…
A Unified Knowledge Graph Augmentation Service for Boosting Domain-specific NLP Tasks
Ruiqing Ding, Xiao Han, Leye Wang
By focusing the pre-training process on domain-specific corpora, some domain-specific pre-trained language models (PLMs) have achieved state-of-the-art results. However, it is unde…
Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment
Jie Zhu, Leye Wang, Xiao Han
The size of deep learning models in artificial intelligence (AI) software is increasing rapidly, which hinders the large-scale deployment on resource-restricted devices (e.g., smar…
Large-Scale Privacy-Preserving Network Embedding against Private Link Inference Attacks
Xiao Han, Leye Wang, Junjie Wu +1
Network embedding represents network nodes by a low-dimensional informative vector. While it is generally effective for various downstream tasks, it may leak some private informati…
Data Valuation for Vertical Federated Learning: A Model-free and Privacy-preserving Method
Xiao Han, Leye Wang, Junjie Wu +1
Vertical Federated learning (VFL) is a promising paradigm for predictive analytics, empowering an organization (i.e., task party) to enhance its predictive models through collabora…
Label Confusion Learning to Enhance Text Classification Models
Biyang Guo, Songqiao Han, Xiao Han +2
Representing a true label as a one-hot vector is a common practice in training text classification models. However, the one-hot representation may not adequately reflect the relati…
Federated Crowdsensing: Framework and Challenges
Leye Wang, Han Yu, Xiao Han
Crowdsensing is a promising sensing paradigm for smart city applications (e.g., traffic and environment monitoring) with the prevalence of smart mobile devices and advanced network…
CreditPrint: Credit Investigation via Geographic Footprints by Deep Learning
Xiao Han, Ruiqing Ding, Leye Wang +1
Credit investigation is critical for financial services. Whereas, traditional methods are often restricted as the employed data hardly provide sufficient, timely and reliable infor…
Geographic Differential Privacy for Mobile Crowd Coverage Maximization
Leye Wang, Gehua Qin, Dingqi Yang +2
For real-world mobile applications such as location-based advertising and spatial crowdsourcing, a key to success is targeting mobile users that can maximally cover certain locatio…