Communication-Efficient Network-Distributed Optimization with Differential-Coded Compressors
dc.contributor.author | Zhang, Xin | |
dc.contributor.author | Zhu, Zhengyuan | |
dc.contributor.author | Liu, Jia | |
dc.contributor.author | Zhu, Zhengyuan | |
dc.contributor.author | Bentley, Elizabeth | |
dc.contributor.department | Computer Science | |
dc.contributor.department | Statistics | |
dc.date | 2020-06-18T03:31:29.000 | |
dc.date.accessioned | 2020-07-02T06:55:44Z | |
dc.date.available | 2020-07-02T06:55:44Z | |
dc.date.embargo | 2020-06-17 | |
dc.date.issued | 2019-01-01 | |
dc.description.abstract | <p>Network-distributed optimization has attracted significant attention in recent years due to its ever-increasing applications. However, the classic decentralized gradient descent (DGD) algorithm is communication-inefficient for large-scale and high-dimensional network-distributed optimization problems. To address this challenge, many compressed DGD-based algorithms have been proposed. However, most of the existing works have high complexity and assume compressors with bounded noise power. To overcome these limitations, in this paper, we propose a new differential-coded compressed DGD (DC-DGD) algorithm. The key features of DC-DGD include: i) DC-DGD works with general SNR-constrained compressors, relaxing the bounded noise power assumption; ii) The differential-coded design entails the same convergence rate as the original DGD algorithm; and iii) DC-DGD has the same low-complexity structure as the original DGD due to a {\em self-noise-reduction effect}. Moreover, the above features inspire us to develop a hybrid compression scheme that offers a systematic mechanism to minimize the communication cost. Finally, we conduct extensive experiments to verify the efficacy of the proposed DC-DGD and hybrid compressor.</p> | |
dc.description.comments | <p>This is a pre-print of the proceeding Zhang, Xin, Jia Liu, Zhengyuan Zhu, and Elizabeth S. Bentley. "Communication-Efficient Network-Distributed Optimization with Differential-Coded Compressors." <em>arXiv preprint arXiv:1912.03208</em> (2019). </p> | |
dc.format.mimetype | application/pdf | |
dc.identifier | archive/lib.dr.iastate.edu/stat_las_conf/14/ | |
dc.identifier.articleid | 1013 | |
dc.identifier.contextkey | 18148074 | |
dc.identifier.s3bucket | isulib-bepress-aws-west | |
dc.identifier.submissionpath | stat_las_conf/14 | |
dc.identifier.uri | https://dr.lib.iastate.edu/handle/20.500.12876/90247 | |
dc.language.iso | en | |
dc.source.bitstream | archive/lib.dr.iastate.edu/stat_las_conf/14/2020_ZhuZhengyuan_CommunicationEfficient.pdf|||Fri Jan 14 20:11:21 UTC 2022 | |
dc.subject.disciplines | Applied Statistics | |
dc.subject.disciplines | Theory and Algorithms | |
dc.title | Communication-Efficient Network-Distributed Optimization with Differential-Coded Compressors | |
dc.type | article | |
dc.type.genre | conference | |
dspace.entity.type | Publication | |
relation.isAuthorOfPublication | 51db2a08-8f9d-4f97-bdbc-6790b3d5a608 | |
relation.isOrgUnitOfPublication | f7be4eb9-d1d0-4081-859b-b15cee251456 | |
relation.isOrgUnitOfPublication | 264904d9-9e66-4169-8e11-034e537ddbca |
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