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

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 …

This article aims to give a comprehensive and rigorous review of the principles and recent
development of coding for large-scale distributed machine learning (DML). With increasing …

Motivated by mobile edge computing and wireless data centers, we study a wireless
distributed computing framework where the distributed nodes exchange information over a …

A secure multi-party batch matrix multiplication problem (SMBMM) is considered, where the
goal is to allow a master to efficiently compute the pairwise products of two batches of …

Coded computation techniques provide robustness against straggling workers in distributed
computing. However, most of the existing schemes require exact provisioning of the …

When gradient descent (GD) is scaled to many parallel workers for large-scale machine
learning applications, its per-iteration computation time is limited by straggling workers …

We present ErasureHead, a new approach for distributed gradient descent (GD) that
mitigates system delays by employing approximate gradient coding. Gradient coded …

Coded computation techniques provide robustness against straggling servers in distributed
computing, with the following limitations: First, they increase decoding complexity. Second …

Distributed computing has become one of the most important frameworks in dealing with
large computation tasks. In this paper, we propose a systematic construction of coded …