Abstract
The combination of genetic programming with federated learning could solve the computational distribution while promoting a collaborative learning environment. This paper proposes a federated learning configuration that enables the use of genetic programming for its global model. In addition, this paper also proposes a new aggregation algorithm that enables the collaborative evolution of genetic programming individuals in federated learning. The case study uses flexible genetic programming, an existing and successful algorithm for image classification, integrated into a federated learning framework. The results show that the use of genetic programming with federated learning achieved a classification error rate of 1.67%, better than the scenario without federated learning, that had an error rate of 3.33%, considering a configuration with three clients with different datasets each.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Szeliski, R.: Computer Vision. Springer International Publishing, Cham (2022)
Markets and Markets: Ai in Computer Vision Market, https://www.marketsandmarkets.com/Market-Reports/ai-in-computer-vision-market-141658064.html
O’Mahony, N., Campbell, S., Carvalho, A., Harapanahalli, S., Hernandez, G.V., Krpalkova, L., Riordan, D., Walsh, J.: Deep learning vs. traditional computer vision. In: CVC 2019: Advances in Computer Vision, pp. 128–144 (2020)
Khan, A., Qureshi, A.S., Wahab, N., Hussain, M., Hamza, M.Y.: A recent survey on the applications of genetic programming in image processing. Comput. Intell. 37, 1745 (2021)
Smith, S.: A Learning System Based on Genetic Adaptive Algorithms. University of Pittsburgh (1980)
Ahvanooey, M., Li, Q., Wu, M., Wang, S.: A survey of genetic programming and its applications. KSII Trans. Internet Inf. Syst. 13 (2019)
Jin, Y., Zhu, H., Xu, J., Chen, Y.: Federated Learning. Springer Nature Singapore, Singapore (2023)
Wen, J., Zhang, Z., Lan, Y., Cui, Z., Cai, J., Zhang, W.: A survey on federated learning: challenges and applications. Int. J. Mach. Learn. Cybern. (2022)
Banabilah, S., Aloqaily, M., Alsayed, E., Malik, N., Jararweh, Y.: Federated learning review: Fundamentals enabling technologies, and future applications. Inf. Process Manag. 59, 103061 (2022)
Doerr, B., Neumann, F.: Theory of Evolutionary Computation. Springer International Publishing, Cham (2020)
Bi, Y., Xue, B., Zhang, M.: Genetic Programming for Image Classification, vol. 24. Springer International Publishing, Cham (2021)
Bi, Y., Xue, B., Zhang, M.: Genetic programming with a new representation to automatically learn features and evolve ensembles for image classification. IEEE Trans. Cybern. 51, 1769 (2021)
Bi, Y., Xue, B., Zhang, M.: Genetic programming with image-related operators and a flexible program structure for feature learning in image classification. IEEE Trans. Evol. Comput. 25, 87 (2021)
Fan, Q., Bi, Y., Xue, B., Zhang, M.: Genetic programming for feature extraction and construction in image classification. Appl. Soft Comput. 118, 108509 (2022)
Pereira, H., Gomes, L., Vale, Z.: Peer-to-peer energy trading optimization in energy communities using multi-agent deep reinforcement learning. Energy Inform. 5, 44 (2022)
Mota, B., Pinto, T., Vale, Z., Ramos, C.: Deep learning in intelligent power and energy systems. In: Intelligent Data Mining and Analysis in Power and Energy Systems, pp. 45–67. Wiley (2022)
Teixeira, N., Barreto, R., Gomes, L., Faria, P., Vale, Z.: A trustworthy building energy management system to enable direct IoT devices’ participation in demand response programs. MDPI Electron. 11, 897 (2022)
Ramos, D., Khorram, M., Faria, P., Vale, Z.: Load forecasting in an office building with different data structure and learning parameters. Forecasting 3, 242 (2021)
Pinto, T., Gomes, L., Faria, P., Vale, Z., Teixeira, N., Ramos, D.: Intelligent simulation and emulation platform for energy management in buildings and microgrids. Mach. Learn. Smart Environ./Cities, 167–181 (2022)
Pu, L.: Fairness of the distribution of public medical and health resources. Front Public Health 9 (2021)
Tong, J., et al.: Distributed learning for heterogeneous clinical data with application to integrating COVID-19 data across 230 sites. NPJ Digit. Med. 5, 76 (2022)
Zhu H., Jin, Y.: Multi-Objective Evolutionary Federated Learning (2018)
Dong, J., Zhong, J., Chen, W.-N., Zhang, J.: An efficient federated genetic programming framework for symbolic regression. IEEE Trans. Emerg. Top Comput. Intell. 1 (2022)
Gong, Y.-J., et al.: Distributed evolutionary algorithms and their models: A survey of the state-of-the-art. Appl. Soft Comput. 34, 286 (2015)
Poli, R.: Parallel Distributed Genetic Programming, in Conference: New Ideas in Optimization (1999)
Jahan, M., Hashem, M.M.A., Shahriar, G.A.: Distributed evolutionary computation: A new technique for solving large number of equations. Int. J. Parallel Distrib. Syst. (2013)
Abdoun, O., Moumen, Y., Abdoun, F.: Parallel evolutionary computation to solve combinatorial optimization problem. In: 2017 International Conference on Electrical and Information Technologies (ICEIT), IEEE, pp. 1–6 (2017)
Fortin, F., Rainville, F., Gardner, M., Parizeau, M., Gagné, C.: DEAP: Evolutionary algorithms made easy. J. Mach. Learn. Res. 13, 2171 (2012)
van der Walt, S., et al.: Scikit-image: Image processing in python. PeerJ 2, e453 (2014)
Virtanen, P., et al.: SciPy 1.0: Fundamental algorithms for scientific computing in python, Nat. Methods 17, 261 (2020)
Beutel, D.J., et al.: Flower: A Friendly Federated Learning Research Framework, (2020)
Acknowledgements
This article is a result of the project RETINA (NORTE-01-0145-FEDER-000062), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). The authors acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team.
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
Ribeiro, B., Gomes, L., Vale, Z. (2023). A Novel Federated Learning Approach to Enable Distributed and Collaborative Genetic Programming. In: Moniz, N., Vale, Z., Cascalho, J., Silva, C., Sebastião, R. (eds) Progress in Artificial Intelligence. EPIA 2023. Lecture Notes in Computer Science(), vol 14116. Springer, Cham. https://doi.org/10.1007/978-3-031-49011-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-49011-8_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-49010-1
Online ISBN: 978-3-031-49011-8
eBook Packages: Computer ScienceComputer Science (R0)