Add to ORKGAs the use of large embedding models in recommendation systems and language applications increases, concerns over user data privacy have also risen. DP-SGD, a training algorithm that combines differential privacy with stochastic gradient descent, has been the workhorse in protecting user privacy without compromising model accuracy by much. However, applying DP-SGD naively to embedding models can destroy gradient sparsity, leading to reduced training efficiency. To address this issue, we present two new algorithms, DP-FEST and DP-AdaFEST, that preserve gradient sparsity during private training of large embedding models. Our algorithms achieve substantial reductions ($10^6 \times$) in gradient size, while maintaining comparable levels of accu...
Using machine learning to improve health care has gained popularity. However, most research in machi...
Large convolutional neural networks (CNN) can be difficult to train in the differentially private (D...
Training even moderately-sized generative models with differentially-private stochastic gradient des...
Differentially private stochastic gradient descent (DP-SGD) has been widely adopted in deep learning...
Differentially Private (DP) learning has seen limited success for building large deep learning model...
Differentially Private methods for training Deep Neural Networks (DNNs) have progressed recently, in...
Preserving privacy in contemporary NLP models allows us to work with sensitive data, but unfortunate...
Differentially private stochastic gradient descent (DP-SGD) is the workhorse algorithm for recent ad...
While modern machine learning models rely on increasingly large training datasets, data is often lim...
A well-known algorithm in privacy-preserving ML is differentially private stochastic gradient descen...
Training large neural networks with meaningful/usable differential privacy security guarantees is a ...
Existing approaches for training neural networks with user-level differential privacy (e.g., DP Fede...
We give simpler, sparser, and faster algorithms for differentially private fine-tuning of large-scal...
Differentially Private Stochastic Gradient Descent (DP-SGD) is a key method for applying privacy in ...
Protecting large language models from privacy leakage is becoming increasingly crucial with their wi...
Using machine learning to improve health care has gained popularity. However, most research in machi...
Large convolutional neural networks (CNN) can be difficult to train in the differentially private (D...
Training even moderately-sized generative models with differentially-private stochastic gradient des...
Differentially private stochastic gradient descent (DP-SGD) has been widely adopted in deep learning...
Differentially Private (DP) learning has seen limited success for building large deep learning model...
Differentially Private methods for training Deep Neural Networks (DNNs) have progressed recently, in...
Preserving privacy in contemporary NLP models allows us to work with sensitive data, but unfortunate...
Differentially private stochastic gradient descent (DP-SGD) is the workhorse algorithm for recent ad...
While modern machine learning models rely on increasingly large training datasets, data is often lim...
A well-known algorithm in privacy-preserving ML is differentially private stochastic gradient descen...
Training large neural networks with meaningful/usable differential privacy security guarantees is a ...
Existing approaches for training neural networks with user-level differential privacy (e.g., DP Fede...
We give simpler, sparser, and faster algorithms for differentially private fine-tuning of large-scal...
Differentially Private Stochastic Gradient Descent (DP-SGD) is a key method for applying privacy in ...
Protecting large language models from privacy leakage is becoming increasingly crucial with their wi...
Using machine learning to improve health care has gained popularity. However, most research in machi...
Large convolutional neural networks (CNN) can be difficult to train in the differentially private (D...
Training even moderately-sized generative models with differentially-private stochastic gradient des...