Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

Intense recent discussions have focused on how to provide individuals with control over
when their data can and cannot be used---the EU's Right To Be Forgotten regulation is an …

We present a communication-efficient surrogate likelihood (CSL) framework for solving
distributed statistical inference problems. CSL provides a communication-efficient surrogate …

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 …

Motivated by the need for distributed learning and optimization algorithms with low
communication cost, we study communication efficient algorithms for distributed mean …

We propose and analyze algorithms to solve a range of learning tasks under user-level
differential privacy constraints. Rather than guaranteeing only the privacy of individual …

We devise a communication-efficient approach to distributed sparse regression in the high-
dimensional setting. The key idea is to average" debiased" or" desparsified" lasso …

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 …

Federated learning (FL) ameliorates privacy concerns in settings where a central server
coordinates learning from data distributed across many clients. The clients train locally and …