Privacy protection of users' entire contribution of samples is important in distributed

Evaluating machine learning models requires evaluation data.

We study the problem of estimating the means of well-separated mixtures when an adversary may add arbitrary outliers.

Privacy and communication constraints are two major bottlenecks in federated learning (FL) and analytics (FA).

Motivated by applications of large embedding models, we study differentially private (DP) optimization problems under sparsity of individual gradients. We start with new near-optimal bounds for the classic mean estimation problem but with sparse data, improving upon existing algorithms particularly for the high-dimensional regime.