Distributed computing has become a common approach for large-scale computation tasks
due to benefits such as high reliability, scalability, computation speed, and cost …

Faced with saturation of Moore's law and increasing size and dimension of data, system
designers have increasingly resorted to parallel and distributed computing to reduce …

We provide novel coded computation strategies for distributed matrix-matrix products that
outperform the recent “Polynomial code” constructions in recovery threshold, ie, the required …

This paper develops coding techniques to reduce the running time of distributed learning
tasks. It characterizes the fundamental tradeoff to compute gradients in terms of three …

In distributed matrix multiplication, a common scenario is to assign each worker a fraction of
the multiplication task, by partitioning the input matrices into smaller submatrices. In …

This paper has two main contributions. First, we propose a novel coding technique-
Generalized PolyDot-for matrix-vector products that advances on existing techniques for …

The goal of coded distributed computation is to efficiently distribute a computation task, such
as matrix multiplication, N-linear computation, or multivariate polynomial evaluation, across …

Federated Learning (FL) is a privacy-preserving collaborative learning approach that trains
artificial intelligence (AI) models without revealing local datasets of the FL workers. While FL …

Large-scale machine learning and data mining applications require computer systems to
perform massive matrix-vector and matrix-matrix multiplication operations that need to be …

Coding for distributed computing supports low-latency computation by relieving the burden
of straggling workers. While most existing works assume a simple master-worker model, we …