189 citations · 386 across the 12 of their papers we have counts for
12 papers
DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction
Xinwei Zhang, Zhiqi Bu, Borja Balle +3
Differential privacy (DP) offers a robust framework for safeguarding individual data privacy. To utilize DP in training modern machine learning models, differentially private optim…
DOPPLER: Differentially Private Optimizers with Low-pass Filter for Privacy Noise Reduction
Xinwei Zhang, Zhiqi Bu, Mingyi Hong +1
Privacy is a growing concern in modern deep-learning systems and applications. Differentially private (DP) training prevents the leakage of sensitive information in the collected t…
Pre-training Differentially Private Models with Limited Public Data
Zhiqi Bu, Xinwei Zhang, Mingyi Hong +2
The superior performance of large foundation models relies on the use of massive amounts of high-quality data, which often contain sensitive, private and copyrighted material that…
Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach
Xinwei Zhang, Zhiqi Bu, Zhiwei Steven Wu +1
Differentially Private Stochastic Gradient Descent with Gradient Clipping (DPSGD-GC) is a powerful tool for training deep learning models using sensitive data, providing both a sol…
GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data
Xinwei Zhang, Mingyi Hong, Jie Chen
Vertical federated learning (VFL) is a distributed learning paradigm, where computing clients collectively train a model based on the partial features of the same set of samples th…
Understanding A Class of Decentralized and Federated Optimization Algorithms: A Multi-Rate Feedback Control Perspective
Xinwei Zhang, Mingyi Hong, Nicola Elia
Distributed algorithms have been playing an increasingly important role in many applications such as machine learning, signal processing, and control. Significant research efforts…