17 citations · 32 across the 5 of their papers we have counts for
5 papers
DP-BREM: Differentially-Private and Byzantine-Robust Federated Learning with Client Momentum
Xiaolan Gu, Ming Li, Li Xiong
Federated Learning (FL) allows multiple participating clients to train machine learning models collaboratively while keeping their datasets local and only exchanging the gradient o…
PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacy
Xiaolan Gu, Ming Li, Li Xiong
Federated Learning (FL) allows multiple participating clients to train machine learning models collaboratively by keeping their datasets local and only exchanging model updates. Ex…
PCKV: Locally Differentially Private Correlated Key-Value Data Collection with Optimized Utility
Xiaolan Gu, Ming Li, Yueqiang Cheng +2
Data collection under local differential privacy (LDP) has been mostly studied for homogeneous data. Real-world applications often involve a mixture of different data types such as…
Providing Input-Discriminative Protection for Local Differential Privacy
Xiaolan Gu, Ming Li, Li Xiong +1
Local Differential Privacy (LDP) provides provable privacy protection for data collection without the assumption of the trusted data server. In the real-world scenario, different d…
A Different Approach to the Problem of Missing Data
Xiao Gu, Norman Matloff
There is a long history of devleopment of methodology dealing with missing data in statistical analysis. Today, the most popular methods fall into two classes, Complete Cases (CC)…