4 citations · 5 across the 8 of their papers we have counts for
10 papers
Amora: Communication-Efficient Private Approximate Nearest Neighbors Search
Qiuhao Wang, Zuowei Chen, Xu Yang +2
Approximate nearest neighbors search (ANNS) over cloud-hosted vector databases raises a critical privacy concern: the query vector, which often encodes sensitive user intent, is ex…
Velocity-coupled Representation Refinement for Satellite Orbit Prediction
Yue Yang, Zhiqiang Wu, Saiyu Qi +1
Satellite orbit prediction, which aims to forecast future orbital trajectories from historical observations, is important for collision warning and safe space operations. With adva…
Verifiable, Efficient and Confidentiality-Preserving Graph Search with Transparency
Qiuhao Wang, Xu Yang, Yiwei Liu +4
Graph databases have garnered extensive attention and research due to their ability to manage relationships between entities efficiently. Today, many graph search services have bee…
NetChain: Authenticated Blockchain Top-k Graph Data Queries and its Application in Asset Management
Hongguang Zhao, Xu Yang, Saiyu Qi +2
As a valuable digital resource, graph data is an important data asset, which has been widely utilized across various fields to optimize decision-making and enable smarter solutions…
Forgetting Through Transforming: Enabling Federated Unlearning via Class-Aware Representation Transformation
Qi Guo, Zhen Tian, Minghao Yao +4
Federated Unlearning (FU) enables clients to selectively remove the influence of specific data from a trained federated learning model, addressing privacy concerns and regulatory r…
Contribution Evaluation of Heterogeneous Participants in Federated Learning via Prototypical Representations
Qi Guo, Minghao Yao, Zhen Tian +4
Contribution evaluation in federated learning (FL) has become a pivotal research area due to its applicability across various domains, such as detecting low-quality datasets, enhan…
SecGraph: Towards SGX-based Efficient and Confidentiality-Preserving Graph Search
Qiuhao Wang, Xu Yang, Saiyu Qi +1
Graphs have more expressive power and are widely researched in various search demand scenarios, compared with traditional relational and XML models. Today, many graph search servic…
Dual Class-Aware Contrastive Federated Semi-Supervised Learning
Qi Guo, Yong Qi, Saiyu Qi +1
Federated semi-supervised learning (FSSL), facilitates labeled clients and unlabeled clients jointly training a global model without sharing private data. Existing FSSL methods pre…
FedMCSA: Personalized Federated Learning via Model Components Self-Attention
Qi Guo, Yong Qi, Saiyu Qi +2
Federated learning (FL) facilitates multiple clients to jointly train a machine learning model without sharing their private data. However, Non-IID data of clients presents a tough…
Stochastic Batch Augmentation with An Effective Distilled Dynamic Soft Label Regularizer
Qian Li, Qingyuan Hu, Yong Qi +3
Data augmentation have been intensively used in training deep neural network to improve the generalization, whether in original space (e.g., image space) or representation space. A…