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 …

Coded distributed computation has become common practice for performing gradient
descent on large datasets to mitigate stragglers and other faults. This paper proposes a …

Distributed computing has emerged as a promising solution for accelerating machine
learning training processes on large-scale datasets by leveraging the parallel processing …

In recent years, Coded Edge Computing (CEC) has been greatly studied as a promising
technology to effectively mitigate the impact of stragglers and provide confidentiality in edge …

One of the main bottlenecks in distributed computing systems is the stragglers' problem.
Error correction codes have been proposed to alleviate this problem at the cost of coding …

The distributed matrix multiplication problem with an unknown number of stragglers is
considered, where the goal is to efficiently and flexibly obtain the product of two massive …

Matrix multiplication is a foundational building block in various data-intensive workloads.
With the fast-increasing sizes of workloads, it is common to split the job of matrix …

Edge computing is considered a promising computing paradigm that can mitigate energy
consumption and workload of end devices through task offloading to edge servers. Albeit …

Coded machine learning is a technique to use codes, such as-maximum-distance-separable
(-MDS) codes, to reduce the negative effect of stragglers by requiring out of workers to …

Matrix multiplication is a crucial operation in many data-intensive workloads. Given the large
size of matrices in today's workloads, it is common to split the computation into tasks …