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Technical Program

Paper Detail

Paper IDD4-S6-T1.1
Paper Title Speeding Up Private Distributed Matrix Multiplication via Bivariate Polynomial Codes
Authors Burak Hasırcıoğlu, Imperial College London, United Kingdom; Jesús Gómez-Vilardebó, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Spain; Deniz Gündüz, Imperial College London, United Kingdom
Session D4-S6-T1: Privacy in Distributed Computation
Chaired Session: Thursday, 15 July, 23:40 - 00:00
Engagement Session: Friday, 16 July, 00:00 - 00:20
Abstract We consider the problem of private distributed matrix multiplication under limited resources. Coded computation has been shown to be an effective solution in distributed matrix multiplication, both providing privacy against the workers and boosting the computation speed by efficiently mitigating stragglers. In this work, we propose the use of recently-introduced bivariate polynomial codes to further speed up private distributed matrix multiplication by exploiting the partial work done by the stragglers rather than completely ignoring them. We show that the proposed approach reduces the average computation time of private distributed matrix multiplication compared to its competitors in the literature while improving the upload communication cost and the workers' storage efficiency.