We focus on some specific problems in distribution testing, taking goodness-of-fit as a
running example. In particular, we do not aim to provide a comprehensive summary of all the …

Privacy and communication constraints are two major bottlenecks in federated learning (FL)
and analytics (FA). We study the optimal accuracy of mean and frequency estimation …

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 …

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 …

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 …

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 study goodness-of-fit and independence testing of discrete distributions in a setting
where samples are distributed across multiple users. The users wish to preserve the privacy …

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

We find separation rates for testing multinomial or more general discrete distributions under
the constraint of alpha-local differential privacy. We construct efficient randomized …

We study simple binary hypothesis testing under local differential privacy (LDP) and
communication constraints. Our results are either minimax optimal or instance optimal: the …