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Securely Straggler-Exploiting Coded Computation for Distributed Matrix Multiplication
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DOI：10.1109/ACCESS.2021.3135581.png)
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Abstract
En 中文
In this paper, we consider coded computation for matrix multiplication tasks in distributed computing, which can mitigate the effect of slow workers, called stragglers, using a coding approach. We assume that the straggler computation results can be leveraged at the master by assigning multiple sub-tasks to the workers. In this scenario, a new coded computation scheme is proposed to preserve the data security and privacy from workers, which is called securely straggler-exploiting codes (SSEC). Moreover, the proposed SSEC can efficiently reduce the communication load in distributed computing for assigning the sub-tasks to the workers, by overlapping the encoded matrices in assigning multiple sub-tasks with appropriate polynomial functions. It is also proven that the data security and privacy constraints are satisfied in SSEC in an information-theoretic sense. In conclusion, SSEC shows good performance on the recovery threshold and communication loads and compare them with the existing secure coded computation schemes for matrix multiplication tasks.
Keywords:
Task analysis
Encoding
Distributed computing
Data privacy
Codes
Data security
Libraries
Distributed computing
coded computation
matrix multiplication
polynomial codes
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W