Most of our lives are conducted in the cyberspace. The human notion of privacy translates
into a cyber notion of privacy on many functions that take place in the cyberspace. This …

Federated learning is a distributed framework according to which a model is trained over a
set of devices, while kee** data localized. This framework faces several systems-oriented …

Codes are widely used in many engineering applications to offer robustness against noise.
In large-scale systems, there are several types of noise that can affect the performance of …

We consider a large-scale matrix multiplication problem where the computation is carried
out using a distributed system with a master node and multiple worker nodes, where each …

We consider the problem of massive matrix multiplication, which underlies many data
analytic applications, in a large-scale distributed system comprising a group of worker …

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 …

Coded computation is a framework for providing redundancy in distributed computing
systems to make them robust to slower nodes, or stragglers. In [1], the authors propose a …

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 …