Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distributed and Private Coded Matrix Computation with Flexible
  Communication Load   Tensor operations, such as matrix multiplication, are central to large-scale
machine learning applications. For user-driven tasks these operations can be
carried out on a distributed computing platform with a master server at the
user side and multiple workers in the cloud operating in parallel. For
distributed platforms, it has been recently shown that coding over the input
data matrices can reduce the computational delay, yielding a trade-off between
recovery threshold and communication load. In this paper we impose an
additional security constraint on the data matrices and assume that workers can
collude to eavesdrop on the content of these data matrices. Specifically, we
introduce a novel class of secure codes, referred to as secure generalized
PolyDot codes, that generalizes previously published non-secure versions of
these codes for matrix multiplication. These codes extend the state-of-the-art
by allowing a flexible trade-off between recovery threshold and communication
load for a fixed maximum number of colluding workers.

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The categories of this paper are: gr-qc
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The categories of this paper are: q-bio
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The categories of this paper are: hep-ph
3
The categories of this paper are: physics
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The categories of this paper are: chao-dyn
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat
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The categories of this paper are: quant-ph
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The categories of this paper are: nlin
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The categories of this paper are: chem-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: atom-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: funct-an
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The categories of this paper are: math-ph
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The categories of this paper are: q-alg