Many common types of data can be represented as functions that map coordinates to signal
values, such as pixel locations to RGB values in the case of an image. Based on this view …

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 …

Relative entropy coding (REC) algorithms encode a sample from a target distribution $ Q $
using a proposal distribution $ P $ using as few bits as possible. Unlike entropy coding, REC …

The efficient communication of noisy data has applications in several areas of machine
learning, such as neural compression or differential privacy, and is also known as reverse …

We study the mean estimation problem under communication and local differential privacy
constraints. While previous work has proposed order-optimal algorithms for the same …

One-shot channel simulation is a fundamental data compression problem concerned with
encoding a single sample from a target distribution $ Q $ using a coding distribution $ P …

We propose two extensions to existing importance sampling based methods for lossy
compression. First, we introduce an importance sampling based compression scheme that is …

One-shot channel simulation (or channel synthesis) has seen increasing applications in
lossy compression, differential privacy and machine learning. In this setting, an encoder …

Compression schemes have been extensively used in Federated Learning (FL) to reduce
the communication cost of distributed learning. While most approaches rely on a bounded …

In this work, we propose a new algorithm ProjectiveGeometryResponse (PGR) for locally
differentially private (LDP) frequency estimation. For universe size of k and with n users, our …