April 2, 2024, 7:12 p.m. | Neophytos Charalambides, Hessam Mahdavifar, Mert Pilanci, Alfred O. Hero III

cs.CR updates on arXiv.org arxiv.org

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 …

applications arxiv asynchronous computing cs.cr cs.dc cs.it cs.lg cs.na distributed distributed computing finance linear machine machine learning math.it math.na problem resiliency security speeding techniques work

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