Visible to the public Distributed and Private Coded Matrix Computation with Flexible Communication Load

TitleDistributed and Private Coded Matrix Computation with Flexible Communication Load
Publication TypeConference Paper
Year of Publication2019
AuthorsAliasgari, Malihe, Simeone, Osvaldo, Kliewer, Jörg
Conference Name2019 IEEE International Symposium on Information Theory (ISIT)
ISBN Number978-1-5386-9291-2
KeywordsCoded distributed computation, colluding workers, computational delay, convolution, data privacy, delays, distributed computing platform, Distributed databases, distributed learning, distributed platforms, encoding, flexible communication load, flexible trade-off, Human Behavior, human factors, information theoretic security, information theoretic security., input data matrices, large-scale machine learning applications, learning (artificial intelligence), master server, matrix multiplication, Metrics, multiple workers, nonsecure versions, policy-based governance, private coded matrix computation, pubcrawl, recovery threshold, resilience, Resiliency, Scalability, secret sharing, secure generalized PolyDot codes, security, security constraint, Servers, telecommunication security, tensor operations

Tensor operations, such as matrix multiplication, are central to large-scale machine learning applications. 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 tradeoff between recovery threshold and communication load. In this work, 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.

Citation Keyaliasgari_distributed_2019