Distributed and Private Coded Matrix Computation with Flexible Communication Load

By Malihe Aliasgari, Osvaldo Simeone, Jörg Kliewer. 2019

34 citations recorded by the source corpus.

Tensor operations, such as matrix multiplication, are central to large-scale machine learning applications. These operations can be carried out on a distributed computing platform with a master server at the user side and multiple workers in the cloud operating in parallel. For distributed platforms, it has been recently shown that coding over the input data matrices can reduce the computational delay, yielding a tradeoff between recovery threshold and communication load. In this work, we impose an additional security constraint on the data matrices and assume that workers can collude to eavesdrop on the content of these data matrices. Specifically, we introduce a novel class of secure codes, referred to as secure generalized PolyDot codes, that generalizes previously published non-secure versions of these codes for matrix multiplication. These codes extend the state-of-the-art by allowing a flexible trade-off between recovery threshold and communication load for a fixed maximum number of colluding workers.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

GASP Codes for Secure Distributed Matrix Multiplication (2018, background): “In [6], the authors consider the SDMM problem with stragglers.”