Automatic Configuration of Federated Learning Client in Graph Classification using Genetic Algorithms

Rezaei, Mohammad; Rezvani, Mohsen; Zahedi, Morteza

doi:10.22044/jadm.2024.13688.2487

Document Type : Original/Review Paper

Authors

Faculty of Computer Engineering, Shahrood University of Technology, Shahrood, Iran.

https://doi.org/10.22044/jadm.2024.13688.2487

Abstract

With the increasing interconnectedness of communications and social networks, graph-based learning techniques offer valuable information extraction from data. Traditional centralized learning methods faced challenges, including data privacy violations and costly maintenance in a centralized environment. To address these, decentralized learning approaches like Federated Learning have emerged. This study explores the significant attention Federated Learning has gained in graph classification and investigates how Model Agnostic Meta-Learning (MAML) can improve its performance, especially concerning non-IID (Non-Independent Identically Distributed) data distributions.

In real-world scenarios, deploying Federated Learning poses challenges, particularly in tuning client parameters and structures due to data isolation and diversity. To address this issue, this study proposes an innovative approach using Genetic Algorithms (GA) for automatic tuning of structures and parameters. By integrating GA with MAML-based clients in Federated Learning, various aspects, such as graph classification structure, learning rate, and optimization function type, can be automatically adjusted. This novel approach yields improved accuracy in decentralized learning at both the client and server levels.

Keywords

Main Subjects

H.3.7. Learning

References

[1] J. Scott, What is social network analysis? Bloomsbury Academic, 2012. [E-book] Avilable: oapen

[2] A. Khazane et al., "DeepTrax: Embedding graphs of financial transactions," in 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), 2019: IEEE, pp. 126-133.

[3] R. Li, Z. Liu, Y. Ma, D. Yang, and S. Sun, "Internet financial fraud detection based on graph learning," IEEE Transactions on Computational Social Systems, 2022.

[4] G. A. Pavlopoulos et al., "Using graph theory to analyze biological networks," BioData mining, vol. 4, no. 1, pp. 1-27, 2011.

[5] A. J. Smola and R. Kondor, "Kernels and regularization on graphs," in Learning theory and kernel machines: Springer, 2003, pp. 144-158.

[6] J. Wen, X. Fang, Y. Xu, C. Tian, and L. Fei, "Low-rank representation with adaptive graph regularization," Neural Networks, vol. 108, pp. 83-96, 2018.

[7] H. Cai, V. W. Zheng, and K. C.-C. Chang, "A comprehensive survey of graph embedding: Problems, techniques, and applications," IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 9, pp. 1616-1637, 2018.

[8] Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip, "A comprehensive survey on graph neural networks," IEEE transactions on neural networks and learning systems, vol. 32, no. 1, pp. 4-24, 2020.

[9] S. Fortunato, "Community detection in graphs," Physics reports, vol. 486, no. 3-5, pp. 75-174, 2010.

[10] S. Sarkar and A. Dong, "Community detection in graphs using singular value decomposition," Physical Review E, vol. 83, no. 4, p. 046114, 2011.

[11] A. Zakrzewska and D. A. Bader, "A dynamic algorithm for local community detection in graphs," in 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2015: IEEE, pp. 559-564.

[12] F. Fouss, A. Pirotte, J.-M. Renders, and M. Saerens, "Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation," IEEE Transactions on knowledge and data engineering, vol. 19, no. 3, pp. 355-369, 2007.

[13] S. Wu, W. Ren, C. Yu, G. Chen, D. Zhang, and J. Zhu, "Personal recommendation using deep recurrent neural networks in NetEase," in 2016 IEEE 32nd international conference on data engineering (ICDE), 2016: IEEE, pp. 1218-1229.

[14] C. C. Noble and D. J. Cook, "Graph-based anomaly detection," in Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, 2003, pp. 631-636.

[15] R. J. Bolton and D. J. Hand, "Statistical fraud detection: A review," Statistical science, vol. 17, no. 3, pp. 235-255, 2002.

[16] Y. Kou, C.-T. Lu, S. Sirwongwattana, and Y.-P. Huang, "Survey of fraud detection techniques," in IEEE International Conference on Networking, Sensing and Control, 2004, 2004, vol. 2: IEEE, pp. 749-754.

[17] Q. Yang, Y. Liu, T. Chen, and Y. Tong, "Federated machine learning: Concept and applications," ACM Transactions on Intelligent Systems and Technology (TIST), vol. 10, no. 2, pp. 1-19, 2019.

[18] C. Chen, Z. Xu, W. Hu, Z. Zheng, and J. Zhang, “FedGL: Federated graph learning framework with global self-supervision,” Information Sciences, vol. 657, pp. 119976, 2024.

[19] P. Kairouz et al., "Advances and open problems in federated learning," Foundations and Trends® in Machine Learning, vol. 14, no. 1–2, pp. 1-210, 2021.

[20] A. Fallah, A. Mokhtari, and A. Ozdaglar, "Personalized federated learning: A meta-learning approach," arXiv preprint arXiv:2002.07948, 2020.

[21] B. Wang, A. Li, H. Li, and Y. Chen, "GraphFL: A federated learning framework for semi-supervised node classification on graphs," arXiv preprint arXiv:2012.04187, 2020.

[22] C. Wu, F. Wu, L. Lyu, T. Qi, Y. Huang, and X. Xie, "A federated graph neural network framework for privacy-preserving personalization," Nature Communications, vol. 13, no. 1, p. 3091, 2022.

[23] S. Ru, B. Zhang, Y. Jie, C. Zhang, L. Wei, and C. Gu, "Graph neural networks for privacy-preserving recommendation with secure hardware," in 2021 International Conference on Networking and Network Applications (NaNA), 2021: IEEE, pp. 395-400.

[24] R. Liu, P. Xing, Z. Deng, A. Li, C. Guan, and H. Yu, "Federated Graph Neural Networks: Overview, Techniques, and Challenges," IEEE Transactions on Neural Networks and Learning Systems, 2024.

[25] Z. Liu, L. Yang, Z. Fan, H. Peng, and P. S. Yu, "Federated social recommendation with graph neural network," ACM Transactions on Intelligent Systems and Technology (TIST), vol. 13, no. 4, pp. 1-24, 2022.

[26] M. Suganuma, S. Shirakawa, and T. Nagao, "A genetic programming approach to designing convolutional neural network architectures," in Proceedings of the genetic and evolutionary computation conference, 2017, pp. 497-504.

[27] S. Gibb, H. M. La, and S. Louis, "A genetic algorithm for convolutional network structure optimization for concrete crack detection," in 2018 IEEE Congress on Evolutionary Computation (CEC), 2018: IEEE, pp. 1-8.

[28] Y. Sun, B. Xue, M. Zhang, G. G. Yen, and J. Lv, "Automatically designing CNN architectures using the genetic algorithm for image classification," IEEE transactions on cybernetics, vol. 50, no. 9, pp. 3840-3854, 2020.

[29] A. Antoniou, H. Edwards, and A. Storkey, "How to train your MAML," arXiv preprint arXiv:1810.09502, 2018.

[30] Z. Falahiazar, A. R. Bagheri, and M. Reshadi, "Determining parameters of DBSCAN Algorithm in Dynamic Environments Automatically using Dynamic Multi-objective Genetic Algorithm," Journal of AI and Data Mining, vol. 10, no. 3, pp. 321-332, 2022. [Online]. Available: www.jad.shahroodut.ac.ir. [Accessed July, 2022].

[31] Y. Zhou, Q. Ye, and J. Lv, "Communication-efficient federated learning with compensated overlap-fedavg," IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 1, pp. 192-205, 2021.

[32] T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, "Federated optimization in heterogeneous networks," Proceedings of Machine learning and systems, vol. 2, pp. 429-450, 2020.

[33] C. Cabanes et al., "The CORA dataset: validation and diagnostics of ocean temperature and salinity in situ measurements," Ocean Science Discussions, vol. 9, no. 2, pp. 1273-1312, 2012.

[34] P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad, "Collective classification in network data," AI magazine, vol. 29, no. 3, pp. 93-93, 2008.

Automatic Configuration of Federated Learning Client in Graph Classification using Genetic Algorithms

References

References

Volume 12, Issue 1January 2024Pages 115-126

Volume 12, Issue 1
January 2024
Pages 115-126