Work

Bivariate Polynomial Codes for Secure Distributed Matrix Multiplication

Year: 2022

Type: article

Abstract: We consider the problem of secure distributed matrix multiplication (SDMM). Coded computation has been shown to be an effective solution in distributed matrix multiplication, both providing privacy against workers and boosting the computation speed by efficiently mitigating stragglers. In this work, we present a non-direct secure extension of the recently introduced bivariate polynomial codes. Bivariate polynomial codes have been shown to be able to further speed up distributed matrix multiplication by exploiting the partial work done by the stragglers rather than completely ignoring them while reducing the upload communication cost and/or the workers’ storage’s capacity needs. We show that, especially for upload communication or storage constrained settings, the proposed approach reduces the average computation time of SDMM compared to its competitors in the literature.

Source: IEEE Journal on Selected Areas in Communications

DOI: 10.1109/jsac.2022.3142355

Authors: Burak Hasirciolu, Jesus Gomez-Vilardebo, Deniz Gunduz

Institutions: Imperial College London, Centre Tecnologic de Telecomunicacions de Catalunya

Language: English

Topic: Cryptography and Data Security

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Engineering and Physical Sciences Research Council, European Research Council, CHIST-ERA

Awards: G6072283711, G7846425480, Communication-Aware Dynamic Edge Computing (CONNECT), G98698327