[go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Learning algorithms for vector quantization using vertically partitioned data with IoT

  • Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

Abstract

The use of cloud computing systems that support AI is expanding. On the other hand, as the number of terminals and devices connected to the system increases, the decline in capacity is becoming apparent. Edge (or fog) computing system is known as one for improving a conventional cloud system. Then, how should machine learning be realized on the edge system? SMC (Secure Multiparty Computation) is known as one model to perform secure learning methods. Horizontally and vertically partitioned data are used as data structure for SMC. There have been proposed some methods of machine learning using horizontally partitioned data of SMC on the edge system. On the other hand, few studies have been done on methods using the vertically partitioned data. In this paper, fast and secure vector quantization algorithms for classification problems on vertically partitioned data with an edge system are proposed. The effectiveness is shown by numerical simulations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Al-Fuqaha A et al (2015) Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun Surv Tutor 17(4):2347–2376

    Article  Google Scholar 

  2. Kitagami S et al (2017) Proposal of a multi-agent based flexible IoT edge computing architecture harmonizing its control with cloud computing. In: The fifth international symposium on computing and networking (CANDAR2017), pp 218–239

  3. Abdelshkour M (2017) IoT, from cloud to fog computing. http://blogs.cisco.com/perspectives/iot-from-cloud-to-fog-computing. Mar 2015. Accessed 18 June 2017

  4. Lopez PG et al (2015) Edge-centric computing: vision and challenges. In: ACM SIGCOMM computer communication review, vol 45, Issue 5, pp 37–42

  5. Sarwar Murshed MG et al (2020) Machine learning at the network edge: a survey. https://arxiv.org/pdf/1908.00080.pdf 2020. Accessed 11 Mar 2020

  6. Chan J, Ran X (2019) Deep learning with edge computing: a review. Proc IEEE 107(8):1655–1674

    Article  Google Scholar 

  7. Aggarwal CC, Yu PS (2009) Privacy-preserving data mining: models and algorithms. ISBN 978-0-387-70991-8. Springer

  8. Miyajima H et al (2016) New privacy preserving back propagation learning for secure multiparty computation. IAENG Int J Comput Sci 43(3):270–276

    Google Scholar 

  9. Yuan J, Yu S (2013) Privacy preserving back-propagation neural network learning made practical with cloud computing. IEEE Trans Parallel Distrib Syst 25(1):212–221

    Article  Google Scholar 

  10. Schlitter N (2008) A protocol for privacy preserving neural network learning on horizontal partitioned data. Privacy statistics in database (PSD)

  11. Miyajima H et al (2018) Proposal of security preserving machine learning of IoT. Artif Intell Res 7(2):26–33

    Article  Google Scholar 

  12. Miyajima H et al (2019) Fast and secure back-propagation learning using vertically partitioned data with IoT. In: Proc. of CANDAR 2019: the seventh international symposium on computing and networking, Nagasaki, pp 450–454

  13. Ruder S (2018) An overview of gradient descent optimization algorithms. http://ruder.io/optimizing-gradient-descent/ 2016. Accessed 14 Mar 2018

  14. Yang Q et al (2019) Federated machine learning: concept and applications. ACM Trans Intell Syst Technol 10(2):12

    Article  Google Scholar 

  15. Miyajima H et al (2019) Proposal of fast and secure clustering method for IoT. In: Proc. of international multiconference of engineers and computer scientists 2019, pp 1–6

  16. Martinetz TM et al (1993) Neural gas network for vector quantization and its application to time-series prediction. IEEE Trans Neural Netw 4(4):558–569

    Article  Google Scholar 

  17. UCI repository of machine learning databases and domain theories. ftp://ftp.ics.uci.edu/pub/machinelearning-Databases

Download references

Acknowledgements

This work was supported by JSPS KAKENHI Grant Number JP17K00170.

Author information

Authors and Affiliations

  1. Okayama University of Science, Okayama, Japan

    Hirofumi Miyajima

  2. Kagoshima University, Kagoshima, Japan

    Noritaka Shigei & Hiromi Miyajima

  3. Chuo University, Tokyo, Japan

    Norio Shiratori

Authors
  1. Hirofumi Miyajima
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Noritaka Shigei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hiromi Miyajima
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Norio Shiratori
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hirofumi Miyajima.

Additional information

This work was presented in part at the 25th International Symposium on Artificial Life and Robotics (Beppu, Oita, January 22–24, 2020).

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Miyajima, H., Shigei, N., Miyajima, H. et al. Learning algorithms for vector quantization using vertically partitioned data with IoT. Artif Life Robotics 26, 283–290 (2021). https://doi.org/10.1007/s10015-021-00683-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-021-00683-1

Keywords

Search

Navigation