Speeding Up Private Distributed Matrix Multiplication via Bivariate Polynomial Codes
This work addresses privacy and efficiency challenges in distributed computing for applications like secure data processing, though it is incremental as it builds on existing coded computation techniques.
The paper tackles the problem of private distributed matrix multiplication under limited resources by proposing bivariate polynomial codes to exploit partial work from stragglers, reducing average computation time, improving upload communication cost, and enhancing workers' storage efficiency compared to existing methods.
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.