The distributed matrix multiplication problem with an unknown number of stragglers is considered, where the goal is to efficiently and flexibly obtain the product of two massive matrices by distributing the computation across N servers. There are up to N - R stragglers but the exact number is not known a priori. Motivated by reducing the computation load of each server, a flexible solution is proposed to fully utilize the computation capability of available servers. The computing task for each server is separated into several subtasks, constructed based on Entangled Polynomial codes by Yu et al. The final results can be obtained from either a larger number of servers with a smaller amount of computation completed per server or a smaller num...
We consider the problems of Private and Secure Matrix Multiplication (PSMM) and Fully Private Matrix...
We consider the problem of secure distributed matrix multiplication (SDMM). Coded computation has be...
Coded distributed computing is an effective framework to improve the speed of distributed computing ...
The distributed matrix multiplication problem with an unknown number of stragglers is considered, wh...
Large matrix multiplications commonly take place in large-scale machine-learning applications. Often...
We consider the problem of secure distributed matrix computation (SDMC), where a user queries a func...
Funding Information: This work has been supported by the Academy of Finland, under Grants No. 318937...
Matrix multiplication is a fundamental building block in many machine learning models. As the input ...
Funding Information: C. Hollanti and J. Li were supported by the Academy of Finland, under Grants No...
We consider the problem of secure distributed matrix computation (SDMC), where a user queries a func...
We consider the distributed computing problem of multiplying a set of vectors with a matrix. For thi...
We consider the problem of private distributed matrix multiplication under limited resources. Coded ...
In this paper, due to the important value in practical applications, we consider the coded distribut...
Coded distributed computing is an effective framework to improve the speed of distributed computing ...
We consider the problem of private distributed matrix multiplication under limited resources. Coded ...
We consider the problems of Private and Secure Matrix Multiplication (PSMM) and Fully Private Matrix...
We consider the problem of secure distributed matrix multiplication (SDMM). Coded computation has be...
Coded distributed computing is an effective framework to improve the speed of distributed computing ...
The distributed matrix multiplication problem with an unknown number of stragglers is considered, wh...
Large matrix multiplications commonly take place in large-scale machine-learning applications. Often...
We consider the problem of secure distributed matrix computation (SDMC), where a user queries a func...
Funding Information: This work has been supported by the Academy of Finland, under Grants No. 318937...
Matrix multiplication is a fundamental building block in many machine learning models. As the input ...
Funding Information: C. Hollanti and J. Li were supported by the Academy of Finland, under Grants No...
We consider the problem of secure distributed matrix computation (SDMC), where a user queries a func...
We consider the distributed computing problem of multiplying a set of vectors with a matrix. For thi...
We consider the problem of private distributed matrix multiplication under limited resources. Coded ...
In this paper, due to the important value in practical applications, we consider the coded distribut...
Coded distributed computing is an effective framework to improve the speed of distributed computing ...
We consider the problem of private distributed matrix multiplication under limited resources. Coded ...
We consider the problems of Private and Secure Matrix Multiplication (PSMM) and Fully Private Matrix...
We consider the problem of secure distributed matrix multiplication (SDMM). Coded computation has be...
Coded distributed computing is an effective framework to improve the speed of distributed computing ...