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

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 …

Coded computation is a method to mitigate “stragglers” in distributed computing systems
through the use of error correction coding that has lately received significant attention. First …

We propose OverSketch, an approximate algorithm for distributed matrix multiplication in
serverless computing. OverSketch leverages ideas from matrix sketching and high …

Motivated by recent developments in serverless systems for large-scale computation as well
as improvements in scalable randomized matrix algorithms, we develop OverSketched …

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

Recently, coding has been a useful technique to mitigate stragglers' effect in distributed
computing. However, coding in this context has been mainly explored assuming …

In the era of artificial intelligence and big data, the demand for data processing has surged,
leading to larger datasets and computation capability. Distributed machine learning (DML) …

Coded distributed computing was recently introduced to mitigate the effect of stragglers on
distributed computing systems. This paper combines ideas of approximate and coded …

Client devices at the edge are generating increasingly large amounts of rich data suitable for
learning powerful statistical models. However, privacy concerns and heavy communication …