@inproceedings{d4e24d36f5b449a7b8566d0711004745,
title = "Preserving Sparsity and Privacy in Straggler-Resilient Distributed Matrix Computations",
abstract = "Existing approaches to distributed matrix computations involve allocating coded combinations of submatrices to worker nodes, to build resilience to stragglers and/or enhance privacy. In this study, we consider the challenge of preserving input sparsity in such approaches to retain the associated computational efficiency enhancements. First, we find a lower bound on the weight of coding, i.e., the number of submatrices to be combined to obtain coded submatrices to provide the resilience to the maximum possible number of stragglers (for given number of nodes and their storage constraints). Next we propose a distributed matrix computation scheme which meets this exact lower bound on the weight of the coding. Further, we develop controllable trade-off between worker computation time and the privacy constraint for sparse input matrices in settings where the worker nodes are honest but curious. Numerical experiments conducted in Amazon Web Services (AWS) validate our assertions regarding straggler mitigation and computation speed for sparse matrices.",
keywords = "Distributed computing, MDS Codes, Privacy, Sparsity, Stragglers",
author = "Das, {Anindya Bijoy} and Aditya Ramamoorthy and Love, {David J.} and Brinton, {Christopher G.}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 59th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2023 ; Conference date: 26-09-2023 Through 29-09-2023",
year = "2023",
doi = "10.1109/Allerton58177.2023.10313473",
language = "English (US)",
series = "2023 59th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 59th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2023",
address = "United States",
}