Iterative Sketching for Secure Coded Regression

arXiv:2308.04185v2 Announce Type: replace-cross
Abstract: Linear regression is a fundamental and primitive problem in supervised machine learning, with applications ranging from epidemiology to finance. In this work, we propose methods for speeding up distributed linear regression. We do so by leveraging randomized techniques, while also ensuring security and straggler resiliency in asynchronous distributed computing systems. Specifically, we randomly rotate the basis of the system of equations and then subsample blocks, to simultaneously secure the information and reduce the dimension of …

abstract applications arxiv asynchronous computing cs.cr cs.dc cs.it cs.lg cs.na distributed distributed computing epidemiology finance iterative linear linear regression machine machine learning math.it math.na regression resiliency security supervised machine learning type work