Analysis on Performance and Energy Consumption in Image Classification with SNN-Based Federated Learning 


Vol. 49,  No. 7, pp. 935-946, Jul.  2024
10.7840/kics.2024.49.7.935


PDF
  Abstract

Federated learning is a cooperative learning process of global neural network models with several devices. Many researchers focus on it due to its advantages in data privacy protection and low communication costs. Most previous studies on federated learning use conventional Artificial Neural Networks (ANNs) as global model. However, it is difficult to use ANNs in mobile and embedded devices because they consume a lot of energy. To apply federate learning to mobile and embedded devices successfully, it is important to use Spiking Neural Networks (SNNs) as global model due to their high energy efficiency. This is because SNNs deliver data between neurons with spikes and the delivery process works in an event-driven manner, which is more energy efficient than that of ANNs. However, the studies on federated learning with SNNs are much less than the studies with ANNs. This problem makes the federated learning with SNNs difficult to be utilized in various applications. In this paper, we conduct simulation with diverse situations and find out that the federated learning with SNNs is more energy efficient than the federated learning with ANNs.

  Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


  Related Articles
  Cite this article

[IEEE Style]

D. Kim, J. Lim, H. Lee, Y. Lim, "Analysis on Performance and Energy Consumption in Image Classification with SNN-Based Federated Learning," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 7, pp. 935-946, 2024. DOI: 10.7840/kics.2024.49.7.935.

[ACM Style]

Dong-gyun Kim, Jae-han Lim, Hyun-jong Lee, and Yeon-sup Lim. 2024. Analysis on Performance and Energy Consumption in Image Classification with SNN-Based Federated Learning. The Journal of Korean Institute of Communications and Information Sciences, 49, 7, (2024), 935-946. DOI: 10.7840/kics.2024.49.7.935.

[KICS Style]

Dong-gyun Kim, Jae-han Lim, Hyun-jong Lee, Yeon-sup Lim, "Analysis on Performance and Energy Consumption in Image Classification with SNN-Based Federated Learning," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 7, pp. 935-946, 7. 2024. (https://doi.org/10.7840/kics.2024.49.7.935)
Vol. 49, No. 7 Index

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)






pISSN : 1226 - 4717
eISSN : 2287 - 3880