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Unification of Deep Learning Model trained by Parallel Learning in Security environment

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2021, 26(12), pp.69-75
  • DOI : 10.9708/jksci.2021.26.12.069
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : November 22, 2021
  • Accepted : December 15, 2021
  • Published : December 31, 2021

LEEJONGLAK 1

1영남이공대학교

Accredited

ABSTRACT

Recently, deep learning, which is the most used in the field of artificial intelligence, has a structure that is gradually becoming larger and more complex. As the deep learning model grows, a large amount of data is required to learn it, but there are cases in which it is difficult to integrate and learn the data because the data is distributed among several owners and security issues. In that situation we conducted parallel learning for each users that own data and then studied how to integrate it. For this, distributed learning was performed for each owner assuming the security situation as V-environment and H-environment, and the results of distributed learning were integrated using Average, Max, and AbsMax. As a result of applying this to the mnist-fashion data, it was confirmed that there was no significant difference from the results obtained by integrating the data in the V-environment in terms of accuracy. In the H-environment, although there was a difference, meaningful results were obtained.

Citation status

* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.