Bivariate Polynomial Codes for Secure Distributed Matrix Multiplication
This work addresses secure and efficient matrix multiplication for distributed computing systems, representing an incremental improvement over prior coded computation methods.
The paper tackles the problem of secure distributed matrix multiplication by extending bivariate polynomial codes to enhance privacy and speed, showing that the proposed approach reduces average computation time compared to existing methods, especially in upload communication or storage constrained settings.
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.