90 citations · 563 across the 40 of their papers we have counts for
7 papers · 1 filter
Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks
Lichao Sun, Congying Xia, Wenpeng Yin +3
Mixup is the latest data augmentation technique that linearly interpolates input examples and the corresponding labels. It has shown strong effectiveness in image classification by…
Federated Model Distillation with Noise-Free Differential Privacy
Lichao Sun, Lingjuan Lyu
Conventional federated learning directly averages model weights, which is only possible for collaboration between models with homogeneous architectures. Sharing prediction instead…
LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy
Lichao Sun, Jianwei Qian, Xun Chen
Train machine learning models on sensitive user data has raised increasing privacy concerns in many areas. Federated learning is a popular approach for privacy protection that coll…
Natural Backdoor Attack on Text Data
Lichao Sun
Recently, advanced NLP models have seen a surge in the usage of various applications. This raises the security threats of the released models. In addition to the clean models' unin…
Secure Deep Graph Generation with Link Differential Privacy
Carl Yang, Haonan Wang, Ke Zhang +2
Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes). Since relational data are often sen…
SplitFed: When Federated Learning Meets Split Learning
Chandra Thapa, M. A. P. Chamikara, Seyit Camtepe +1
Federated learning (FL) and split learning (SL) are two popular distributed machine learning approaches. Both follow a model-to-data scenario; clients train and test machine learni…