Two major challenges in distributed learning and estimation are 1) preserving the privacy of
the local samples; and 2) communicating them efficiently to a central server, while achieving …

When the data are stored in a distributed manner, direct applications of traditional statistical
inference procedures are often prohibitive due to communication costs and privacy …

Compressing the output of $\epsilon $-locally differentially private (LDP) randomizers
naively leads to suboptimal utility. In this work, we demonstrate the benefits of using …

We consider the problem of learning high-dimensional, nonparametric and structured (eg,
Gaussian) distributions in distributed networks, where each node in the network observes an …

Multiple players are each given one independent sample, about which they can only provide
limited information to a central referee. Each player is allowed to describe its observed …

We consider parameter estimation in distributed networks, where each sensor in the network
observes an independent sample from an underlying distribution and has $ k $ bits to …

We study the role of interactivity in distributed statistical inference under information
constraints, eg, communication constraints and local differential privacy. We focus on the …

A central server needs to perform statistical inference based on samples that are distributed
over multiple users who can each send a message of limited length to the center. We study …

We consider distributed parameter estimation using interactive protocols subject to local
information constraints such as bandwidth limitations, local differential privacy, and restricted …

Multiple users getting one sample each from an unknown distribution seek to enable a
central server to conduct statistical inference. However, each player can only provide limited …