We give simpler, sparser, and faster algorithms for differentially private fine-tuning of large-
scale pre-trained language models, which achieve the state-of-the-art privacy versus utility …

Federated learning aims to collaboratively train a strong global model by accessing users'
locally trained models but not their own data. A crucial step is therefore to aggregate local …

We explore the feasibility of using Generative Adversarial Networks (GANs) to automatically
learn generative models to generate synthetic packet-and flow header traces for networking …

In the privacy-utility tradeoff of a model trained on benchmark language and vision tasks,
remarkable improvements have been widely reported when the model is pretrained on …

Federated learning (FL) enables clients to collaborate with a server to train a machine
learning model. To ensure privacy, the server performs secure aggregation of updates from …

Differentially private SGD (DP-SGD) is one of the most popular methods for solving
differentially private empirical risk minimization (ERM). Due to its noisy perturbation on each …

We initiate the study of differentially private (DP) estimation with access to a small amount of
public data. For private estimation of $ d $-dimensional Gaussians, we assume that the …

In this paper, we revisit the problem of using in-distribution public data to improve the
privacy/utility trade-offs for differentially private (DP) model training.(Here, public data refers …

We study private synthetic data generation for query release, where the goal is to construct a
sanitized version of a sensitive dataset, subject to differential privacy, that approximately …

We study the problem of private distribution learning with access to public data. In this setup,
which we refer to as* public-private learning*, the learner is given public and private …