The stringent requirements for low-latency and privacy of the emerging high-stake
applications with intelligent devices such as drones and smart vehicles make the cloud …

We consider a scenario involving computations over a massive dataset stored distributedly
across multiple workers, which is at the core of distributed learning algorithms. We propose …

The appeal of serverless (FaaS) has triggered a growing interest on how to use it in data-
intensive applications such as ETL, query processing, or machine learning (ML). Several …

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 …

In large-scale distributed computing clusters, such as Amazon EC2, there are several types
of “system noise” that can result in major degradation of performance: system failures …

Federated learning enables training a global model from data located at the client nodes,
without data sharing and moving client data to a centralized server. Performance of …

Gradient coding is a technique for straggler mitigation in distributed learning. In this paper
we design novel gradient codes using tools from classical coding theory, namely, cyclic …

Due to the large size of the training data, distributed learning approaches such as federated
learning have gained attention recently. However, the convergence rate of distributed …

Abstract Distributed Stochastic Gradient Descent (SGD) when run in a synchronous manner,
suffers from delays in waiting for the slowest learners (stragglers). Asynchronous methods …