We introduce the concept of “coded computing”, a novel computing paradigm that utilizes
coding theory to effectively inject and leverage data/computation redundancy to mitigate …

We consider distributed gradient descent in the presence of stragglers. Recent work on
gradient coding and approximate gradient coding have shown how to add redundancy in …

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 …

One of the major challenges in using distributed learning to train complicated models with
large data sets is to deal with stragglers effect. As a solution, coded computation has been …

In this paper, we propose CodedSketch, as a distributed straggler-resistant scheme to
compute an approximation of the multiplication of two massive matrices. The objective is to …

Distributed Stochastic Gradient Descent (SGD) when run in a synchronous manner, suffers
from delays in runtime as it waits for the slowest workers (stragglers). Asynchronous …

This paper addresses the gradient coding and coded matrix multiplication problems in
distributed optimization and coded computing. We present a computationally efficient coding …

Federated Learning is an emerging learning paradigm that allows training models from
samples distributed across a large network of clients while respecting privacy and …

Gradient codes use data replication to mitigate the effect of straggling machines in
distributed machine learning. Approximate gradient codes consider codes where the data …