Coded computation of multiple functions
We consider the problem of evaluating arbitrary multivariate polynomials over several massive datasets in a distributed computing system with a single master node and multiple worker nodes. We focus on the general case when each multivariate polynomial is evaluated over its dataset and propose a...
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| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/165833 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | We consider the problem of evaluating arbitrary multivariate polynomials over
several massive datasets in a distributed computing system with a single master
node and multiple worker nodes. We focus on the general case when each
multivariate polynomial is evaluated over its dataset and propose a
generalization of the Lagrange Coded Computing framework (Yu et al. 2019) to
provide robustness against stragglers who do not respond in time, adversarial
workers who respond with wrong computation and information-theoretic security
of dataset against colluding workers. Our scheme introduces a small computation
overhead which results in a reduction in download cost and also offers
comparable resistance to stragglers over existing solutions. |
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