On data summarization for machine learning in multi-organization federations

Bong Jun Ko, Shiqiang Wang, Ting He, Dave Conway-Jones

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

1 Scopus citations

Abstract

Machine learning is a promising technology for many modern applications. To train an effective machine learning model, a large amount of data is required. However, data may be created in different organizations and sharing data across organizational boundaries is difficult due to privacy concerns and communication bandwidth limitations. Data summarization is a technique for reducing the amount of data that needs to be shared, while preserving characteristics in the data that are useful for training machine learning models. In this paper, we present an overview of data summarization techniques, which can be useful for machine learning across organizational boundaries. We also discuss some possible applications related to these data summarization techniques and challenges for future research.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE International Conference on Smart Computing, SMARTCOMP 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages63-68
Number of pages6
ISBN (Electronic)9781728116891
DOIs
StatePublished - Jun 2019
Event5th IEEE International Conference on Smart Computing, SMARTCOMP 2019 - Washington, United States
Duration: Jun 12 2019Jun 14 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Smart Computing, SMARTCOMP 2019

Conference

Conference5th IEEE International Conference on Smart Computing, SMARTCOMP 2019
CountryUnited States
CityWashington
Period6/12/196/14/19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality

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