Abstract
During the last decade, Industrial Cyber-Physical Systems (ICPS) have attracted a significant amount of interest from industries as well as academic institutions. These kinds of systems have proved to be very complicated, and it may be a difficult task to get a handle on their architecture and make sure everything works properly. By putting up a framework for federated learning that we’ve dubbed FedGA-ICPS the purpose of this study is to address some of the difficulties that are associated with the performance and decision-making aids provided by ICPS. To begin, we launch an ICPS modeling formalism with the goal of specifying the structure and behaviour of such systems. FedGA-ICPS then conducts an analysis of the performance of the industrial sensors based on the data supplied by the ICPS from the industrial sensors by putting forth locally integrated learning models. Following that, a genetic algorithm drives federated learning in order to quicken and enhance the aggregation process. In the end, transfer learning is used so that the learned parameters of the models may be distributed across a variety of limited entities. FedGA-ICPS has been implemented on MNIST, and the results have been rather significant.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics, pp. 1273–1282. PMLR (2017)
Arivazhagan, M.G., Aggarwal, V., Singh, A.K., Choudhary, S.: Federated learning with personalization layers. arXiv preprint arXiv:1912.00818 (2019)
Ek, S., Portet, F., Lalanda, P., Vega, G.: A federated learning aggregation algorithm for pervasive computing: evaluation and comparison. In 19th IEEE International Conference on Pervasive Computing and Communications, PerCom (2021)
Połap, D., Srivastava, G., Yu, K.: Agent architecture of an intelligent medical system based on federated learning and blockchain technology. J. Inf. Secur. Appl. 58, 102748 (2021)
Tian, P., Chen, Z., Yu, W., Liao, W.: Towards asynchronous federated learning based threat detection: a DC-Adam approach. Comput. Secur. 108, 102344 (2021)
Chen, Y., Qin, X., Wang, J., Yu, C., Gao, W.: FedHealth: a federated transfer learning framework for wearable healthcare. IEEE Intell. Syst. 35(4), 83–93 (2020)
Hao, M., Li, H., Luo, X., Xu, G., Yang, H., Liu, S.: Efficient and privacy-enhanced federated learning for industrial artificial intelligence. IEEE Trans. Ind. Inform. 16(10), 6532–6542 (2019)
Zhou, C., Fu, A., Yu, S., Yang, W., Wang, H., Zhang, Y.: Privacy-preserving federated learning in fog computing. IEEE Internet Things J. 7(11), 10782–10793 (2020)
Huang, S.-C., Le, T.-H.: Chapter 4 - multi-category classification problem. In: Huang, S.-C., Le, T.-H. (eds.) Principles and Labs for Deep Learning, pp. 81–116. Academic Press (2021)
Zhu, W., Ma, Y., Zhou, Y., Benton, M., Romagnoli, J.: Deep learning based soft sensor and its application on a pyrolysis reactor for compositions predictions of gas phase components. In: 13th International Symposium on Process Systems Engineering (PSE 2018), vol. 44, pp. 2245–2250. Elsevier (2018)
Sun, T., Li, D., Wang, B.: Decentralized federated averaging. arXiv preprint arXiv:2104.11375 (2021)
Deng, L.: The MNIST database of handwritten digit images for machine learning research. IEEE Signal Process. Mag. 29(6), 141–142 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Guendouzi, S.B., Ouchani, S., Malki, M. (2023). Genetic Algorithm Based Aggregation for Federated Learning in Industrial Cyber Physical Systems. In: García Bringas, P., et al. International Joint Conference 15th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2022) 13th International Conference on EUropean Transnational Education (ICEUTE 2022). CISIS ICEUTE 2022 2022. Lecture Notes in Networks and Systems, vol 532. Springer, Cham. https://doi.org/10.1007/978-3-031-18409-3_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-18409-3_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-18408-6
Online ISBN: 978-3-031-18409-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)