Communication efficient distributed mean estimation is an important primitive that arises in
many distributed learning and optimization scenarios such as federated learning. Without …

Consider a distributed multi-task learning (MTL) framework where the distributed users first
train their own models based on the local data and then send the local updates to the server …

We study the problem of communication-efficient distributed vector mean estimation, a
commonly used subroutine in distributed optimization and Federated Learning (FL). Rand …

The goal of this thesis is to study the compression problems arising in distributed computing
systematically. In the first part of the thesis, we study gradient compression for distributed first …

In this paper, we consider a hierarchical distributed multi-task learning (MTL) system where
distributed users wish to jointly learn different models orchestrated by a central server with …

Consider distributed optimization of smooth convex functions over ℝ d where K independent
clients can provide estimates of the gradient. Assume that all the gradient estimates are …

Communication efficient distributed mean estimation is an important primitive that arises in
many distributed learning and optimization scenarios such as federated learning. Without …