4 papers · 1 filter
Towards hyperparameter-free optimization with differential privacy
Zhiqi Bu, Ruixuan Liu
Differential privacy (DP) is a privacy-preserving paradigm that protects the training data when training deep learning models. Critically, the performance of models is determined b…
Zero redundancy distributed learning with differential privacy
Zhiqi Bu, Justin Chiu, Ruixuan Liu +2
Deep learning using large models have achieved great success in a wide range of domains. However, training these models on billions of parameters is very challenging in terms of th…
On the accuracy and efficiency of group-wise clipping in differentially private optimization
Zhiqi Bu, Ruixuan Liu, Yu-Xiang Wang +2
Recent advances have substantially improved the accuracy, memory cost, and training speed of differentially private (DP) deep learning, especially on large vision and language mode…
Coupling public and private gradient provably helps optimization
Ruixuan Liu, Zhiqi Bu, Yu-xiang Wang +2
The success of large neural networks is crucially determined by the availability of data. It has been observed that training only on a small amount of public data, or privately on…