9 citations · 11 across the 10 of their papers we have counts for
4 papers · 1 filter
Privacy-Preserving Federated Learning via Dataset Distillation
ShiMao Xu, Xiaopeng Ke, Xing Su +4
Federated Learning (FL) allows users to share knowledge instead of raw data to train a model with high accuracy. Unfortunately, during the training, users lose control over the kno…
If LLMs Would Just Look: Simple Line-by-line Checking Improves Vulnerability Localization
Yue Li, Xiao Li, Hao Wu +5
The rapid expansion of software systems and the growing number of reported vulnerabilities have emphasized the importance of accurately identifying vulnerable code segments. Tradit…
Training Data Attribution: Was Your Model Secretly Trained On Data Created By Mine?
Likun Zhang, Hao Wu, Lingcui Zhang +4
The emergence of text-to-image models has recently sparked significant interest, but the attendant is a looming shadow of potential infringement by violating the user terms. Specif…
CoAst: Validation-Free Contribution Assessment for Federated Learning based on Cross-Round Valuation
Hao Wu, Likun Zhang, Shucheng Li +2
In the federated learning (FL) process, since the data held by each participant is different, it is necessary to figure out which participant has a higher contribution to the model…