4 citations · 5 across the 6 of their papers we have counts for
7 papers
Privet: A Privacy-Preserving Vertical Federated Learning Service for Gradient Boosted Decision Tables
Yifeng Zheng, Shuangqing Xu, Songlei Wang +2
Vertical federated learning (VFL) has recently emerged as an appealing distributed paradigm empowering multi-party collaboration for training high-quality models over vertically pa…
Shielding Federated Learning: Mitigating Byzantine Attacks with Less Constraints
Minghui Li, Wei Wan, Jianrong Lu +5
Federated learning is a newly emerging distributed learning framework that facilitates the collaborative training of a shared global model among distributed participants with their…
OblivGM: Oblivious Attributed Subgraph Matching as a Cloud Service
Songlei Wang, Yifeng Zheng, Xiaohua Jia +2
In recent years there has been growing popularity of leveraging cloud computing for storing and querying attributed graphs, which have been widely used to model complex structured…
Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization
Yifeng Zheng, Shangqi Lai, Yi Liu +3
Federated learning has recently emerged as a paradigm promising the benefits of harnessing rich data from diverse sources to train high quality models, with the salient features th…
Optimizing Secure Decision Tree Inference Outsourcing
Yifeng Zheng, Cong Wang, Ruochen Wang +2
Outsourcing decision tree inference services to the cloud is highly beneficial, yet raises critical privacy concerns on the proprietary decision tree of the model provider and the…
SEDML: Securely and Efficiently Harnessing Distributed Knowledge in Machine Learning
Yansong Gao, Qun Li, Yifeng Zheng +3
Training high-performing deep learning models require a rich amount of data which is usually distributed among multiple data sources in practice. Simply centralizing these multi-so…