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 …

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 …

The traditional approach in FL tries to learn a single global model collaboratively with the
help of many clients under the orchestration of a central server. However, learning a single …

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 …

Federated learning (FL) is a privacy-preserving machine learning setting that enables many
devices to jointly train a shared global model without the need to reveal their data to a …

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

Despite the many advantages of using deep neural networks over shallow networks in
various machine learning tasks, their effectiveness is compromised in a federated learning …

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 …

The large communication cost for exchanging gradients between different nodes
significantly limits the scalability of distributed training for large-scale learning models …