182 citations · 356 across the 13 of their papers we have counts for
4 papers · 1 filter
Can Public Large Language Models Help Private Cross-device Federated Learning?
Boxin Wang, Yibo Jacky Zhang, Yuan Cao +5
We study (differentially) private federated learning (FL) of language models. The language models in cross-device FL are relatively small, which can be trained with meaningful form…
FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data
Wenda Chu, Chulin Xie, Boxin Wang +5
Federated learning (FL) allows agents to jointly train a global model without sharing their local data. However, due to the heterogeneous nature of local data, it is challenging to…
Improving Certified Robustness via Statistical Learning with Logical Reasoning
Zhuolin Yang, Zhikuan Zhao, Boxin Wang +8
Intensive algorithmic efforts have been made to enable the rapid improvements of certificated robustness for complex ML models recently. However, current robustness certification m…
Towards Efficient Data Valuation Based on the Shapley Value
Ruoxi Jia, David Dao, Boxin Wang +7
"How much is my data worth?" is an increasingly common question posed by organizations and individuals alike. An answer to this question could allow, for instance, fairly distribut…