Central versus Distributed Statistical Computing Algorithms-A Comparison

Nisha Thorakkattu Madathil, Saad Harous

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Distributed statistical learning algorithms are performing many machine learning tasks in a distributed environment. Some scenarios where data sharing is desired among many parties and it may need to increase the efficiency and statistical accuracy of the underlying algorithms. Due to the increase in the size and complexity of today's big data, it is very important to solve problems with a very large number of features, records, and training samples. As a result, it is necessary to deal with the distributed transfer of these datasets as well as their underlying distributed solution methods efficiently and effectively. This paper compares the efficiency and accuracy of a distributed statistical method with a central method with simple regression and classification algorithms.

Original languageEnglish
Title of host publication2020 11th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2020
EditorsRajashree Paul
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages87-92
Number of pages6
ISBN (Electronic)9781728196565
DOIs
Publication statusPublished - Oct 28 2020
Event11th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2020 - Virtual, New York City, United States
Duration: Oct 28 2020Oct 31 2020

Publication series

Name2020 11th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2020

Conference

Conference11th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2020
Country/TerritoryUnited States
CityVirtual, New York City
Period10/28/2010/31/20

Keywords

  • Split and merge
  • distributed statistical learning
  • linear regression
  • logistic regression

ASJC Scopus subject areas

  • Computer Science Applications
  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Computer Networks and Communications

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