Distributed Stochastic Gradient Descent Using LDGM Codes

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

We consider a distributed learning problem in which the computation is carried out on a system consisting of a master node and multiple worker nodes. In such systems, the existence of slow-running machines called stragglers will cause a significant decrease in performance. Recently, coding theoretic framework, which is named Gradient Coding (GC), for mitigating stragglers in distributed learning has been established by Tandon et al. Most studies on GC are aiming at recovering the gradient information completely assuming that the Gradient Descent (GD) algorithm is used as a learning algorithm. On the other hand, if the Stochastic Gradient Descent (SGD) algorithm is used, it is not necessary to completely recover the gradient information, and its unbiased estimator is sufficient for the learning. In this paper, we propose a distributed SGD scheme using Low Density Generator Matrix (LDGM) codes. In the proposed system, it may take longer time than existing GC methods to recover the gradient information completely, however, it enables the master node to obtain a high-quality unbiased estimator of the gradient at low computational cost and it leads to overall performance improvement.

本文言語English
ホスト出版物のタイトル2019 IEEE International Symposium on Information Theory, ISIT 2019 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1417-1421
ページ数5
ISBN(電子版)9781538692912
DOI
出版ステータスPublished - 2019 7
イベント2019 IEEE International Symposium on Information Theory, ISIT 2019 - Paris, France
継続期間: 2019 7 72019 7 12

出版物シリーズ

名前IEEE International Symposium on Information Theory - Proceedings
2019-July
ISSN(印刷版)2157-8095

Conference

Conference2019 IEEE International Symposium on Information Theory, ISIT 2019
国/地域France
CityParis
Period19/7/719/7/12

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • 情報システム
  • モデリングとシミュレーション
  • 応用数学

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