Skip to main content

Analysis of Techniques for Mapping Convolutional Neural Networks onto Cloud Edge Architectures Using SplitFed Learning Method

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 451)

Abstract

The Convolutional Neural Network is a machine learning algorithm of increasing interest in recent years for its use in computer vision. Today, there are a lot of applications in safe driving, object recognition, person identification and in healthcare. On the other hand, many devices do not have the computational power to support a deep neural network and, moreover, a machine learning algorithm requires a training set of considerable size for optimization, which is continuously updated and common to multiple users. The shared data relating to the images, can generate security problems to the system by falling within the field of data privacy. Local regulations, such as the GDPR in Europe, provide for high levels of security, in particular data defined “sensitive”, such as biometrics and health data. Using this data in a shared environment can lead to a data breach, not sharing it degrades CNN’s performance. In this article we will illustrate the mechanisms of subdivision of a convolutional neural network between edge devices, with limited computational power and a public cloud platform. The distribution of the computation aims at convolution of the neural network and at preserving system security. In particular, an example of distribution will be illustrated using the tree-computation pattern on SplitFed Learning architecture.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-99619-2_16
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   219.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-99619-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   279.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.

Notes

  1. 1.

    https://www.smartbuildingitalia.it/wp-content/uploads/2019/11/mattia-bastianini-.pdf.

References

  1. Beaufays, F.S., Chen, M., Mathews, R., Ouyang, T.: Federated learning of out-of-vocabulary words (2019)

    Google Scholar 

  2. Cantiello, P., Di Martino, B., Mastroianni, M., Cante, L.C., Graziano, M.: Towards a cloud model choice evaluation: comparison between cost/features and ontology-based analysis. Int. J. Grid Util. Comput. (2022, article published/in press). http://hdl.handle.net/2122/15035

  3. Chishti, S.O.A., Riaz, S., BilalZaib, M., Nauman, M.: Self-driving cars using CNN and Q-learning. In: 2018 IEEE 21st International Multi-Topic Conference (INMIC), pp. 1–7. IEEE (2018)

    Google Scholar 

  4. Di Martino, B., Colucci Cante, L., Graziano, M., Enrich Sard, R.: Tweets analysis with big data technology and machine learning to evaluate smart and sustainable urban mobility actions in Barcelona. In: Barolli, L., Poniszewska-Maranda, A., Enokido, T. (eds.) CISIS 2020. AISC, vol. 1194, pp. 510–519. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-50454-0_53

    CrossRef  Google Scholar 

  5. Di Martino, B., Cascone, D., Colucci Cante, L., Esposito, A.: Semantic representation and rule based patterns discovery and verification in eProcurement business processes for eGovernment. In: Barolli, L., Yim, K., Enokido, T. (eds.) CISIS 2021. LNNS, vol. 278, pp. 667–676. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79725-6_67

    CrossRef  Google Scholar 

  6. Di Martino, B., Esposito, A.: Applying patterns to support deployment in cloud-edge environments: a case study. In: Barolli, L., Woungang, I., Enokido, T. (eds.) AINA 2021. LNNS, vol. 227, pp. 139–148. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75078-7_15

    CrossRef  Google Scholar 

  7. Goericke, S.: Using convolution neural networks to develop robust combat behaviors through reinforcement learning. Ph.D. thesis, Naval Postgraduate School (2021)

    Google Scholar 

  8. He, C., Annavaram, M., Avestimehr, S.: Group knowledge transfer: federated learning of large CNNs at the edge. arXiv preprint arXiv:2007.14513 (2020)

  9. Kwasigroch, A., Jarzembinski, B., Grochowski, M.: Deep CNN based decision support system for detection and assessing the stage of diabetic retinopathy. In: 2018 International Interdisciplinary PhD Workshop (IIPhDW), pp. 111–116. IEEE (2018)

    Google Scholar 

  10. Lu, Y., Fan, L.: An efficient and robust aggregation algorithm for learning federated CNN. In: Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning, pp. 1–7 (2020)

    Google Scholar 

  11. Thapa, C., Chamikara, M.A.P., Camtepe, S., Sun, L.: SplitFed: when federated learning meets split learning. arXiv preprint arXiv:2004.12088 (2020)

  12. Thapa, C., Chamikara, M.A.P., Camtepe, S.A.: Advancements of federated learning towards privacy preservation: from federated learning to split learning. In: Rehman, M.H., Gaber, M.M. (eds.) Federated Learning Systems. SCI, vol. 965, pp. 79–109. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-70604-3_4

    CrossRef  Google Scholar 

  13. Truex, S., et al.: A hybrid approach to privacy-preserving federated learning. In: Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security, pp. 1–11 (2019)

    Google Scholar 

  14. Wang, S.C.: Artificial neural network. In: Interdisciplinary Computing in Java Programming, pp. 81–100. Springer, Cham (2003). https://doi.org/10.1007/978-1-4615-0377-4_5

  15. Zhang, Z.: Artificial neural network. In: Multivariate Time Series Analysis in Climate and Environmental Research, pp. 1–35. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-67340-0_1

Download references

Acknowledgements

The work described in this paper has been supported by the Project VALERE “SSCeGov - Semantic, Secure and Law Compliant e-Government Processes”.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Beniamino Di Martino .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Di Martino, B., Graziano, M., Colucci Cante, L., Cascone, D. (2022). Analysis of Techniques for Mapping Convolutional Neural Networks onto Cloud Edge Architectures Using SplitFed Learning Method. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 451. Springer, Cham. https://doi.org/10.1007/978-3-030-99619-2_16

Download citation