Abstract: In this talk, we will present highlights from some of the work we have been doing in distributed inference under information constraints, such as privacy and communication. We consider basic tasks such as learning and testing of discrete as well as high dimensional distributions, when the samples are distributed across users who can then only send an information-constrained message about their sample. Of key interest to us has been the role of the various types of communication protocols (e.g., non-interactive protocols…