Secure Linear MDS Coded Matrix Inversion
Abstract
A cumbersome operation in many scientific fields, is inverting large full-rank matrices. In this paper, we propose a coded computing approach for recovering matrix inverse approximations. We first present an approximate matrix inversion algorithm which does not require a matrix factorization, but uses a black-box least squares optimization solver as a subroutine, to give an estimate of the inverse of a real full-rank matrix. We then present a distributed framework for which our algorithm can be implemented, and show how we can leverage sparsest-balanced MDS generator matrices to devise matrix inversion coded computing schemes. We focus on balanced Reed-Solomon codes, which are optimal in terms of computational load; and communication from the workers to the master server. We also discuss how our algorithms can be used to compute the pseudoinverse of a full-rank matrix, and how the communication is secured from eavesdroppers.
Cite
@article{arxiv.2207.06271,
title = {Secure Linear MDS Coded Matrix Inversion},
author = {Neophytos Charalambides and Mert Pilanci and Alfred Hero},
journal= {arXiv preprint arXiv:2207.06271},
year = {2022}
}