Optimizing Data Transfer and Convergence Time for Federated Learning based on NSGA II

Document Type : Original Article

Authors

Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.

Abstract

Face recognition from digital images is used for surveillance and authentication in cities, organizations, and personal devices. Internet of Things (IoT)-powered face recognition systems use multiple sensors and one or more servers to process data. All sensor data from initial methods was sent to the central server for processing, raising concerns about sensitive data disclosure. The main concern was that all data from all sectors that could contain confidential information was placed in a central server. Federated learning can solve this problem by using several local model training servers for each region and a central aggregation server to form a global model in IoT networks. This article presents a novel approach to optimize data transfer and convergence time in federated learning for a face recognition task using Non-dominated Sorting Genetic Algorithm II (NSGA II). The aim of the study is to balance the trade-off between training time and model accuracy in a federated learning environment. The results demonstrate the effectiveness of the proposed approach in reducing data transfer and convergence time, leading to improved performance in face recognition accuracy. This research provides insights for researchers and practitioners to enhance the efficiency of federated learning in real-world applications.

Keywords

References

[1] A. K. Agrawal and Y. N. Singh, "Evaluation of face recognition methods in unconstrained environments," Procedia Computer Science, vol. 48, pp. 644-651, 2015.
[2] P. Kaur, K. Krishan, S. K. Sharma, and T. Kanchan, "Facial-recognition algorithms: A literature review,"  Medicine, Science and the Law, vol. 60, no. 2, pp. 131-139, 2020.
[3] S. Chen, A. Pande, and P. Mohapatra, "Sensor-assisted facial recognition: an enhanced biometric authentication system for smartphones," in Proceedings of the 12th annual international conference on Mobile systems, applications, and services, 2014, pp. 109-122.
[4] M. Dabbah, W. Woo, and S. Dlay, "Secure authentication for face recognition," in 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, 2007: IEEE, pp. 121-126.
[5] S. Qiao, C. Liu, W. Shen, and A. L. Yuille, "Few-shot image recognition by predicting parameters from activations," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 7229-7238.
[6] R.-Q. Wang, X.-Y. Zhang, and C.-L. Liu, "Meta-prototypical learning for domain-agnostic few-shot recognition," IEEE Transactions on Neural Networks and Learning Systems, 2021.
[7] K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan, "A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II," in International conference on parallel problem solving from nature, 2000: Springer, pp. 849-858.
[8] H. Li and Q. Zhang, "Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II," IEEE transactions on evolutionary computation, vol. 13, no. 2, pp. 284-302, 2008.
[9] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," IEEE transactions on evolutionary computation, vol. 6, no. 2, pp. 182-197, 2002.
[10] Y. Yusoff, M. S. Ngadiman, and A. M. Zain, "Overview of NSGA-II for optimizing machining process parameters," Procedia Engineering, vol. 15, pp. 3978-3983, 2011.
[11] S. Verma, M. Pant, and V. Snasel, "A comprehensive review on NSGA-II for multi-objective combinatorial optimization problems," Ieee Access, vol. 9, pp. 57757-57791, 2021.
[12] R. Zhao, Y. Wang, Z. Xue, T. Ohtsuki, B. Adebisi, and G. Gui, "Semi-Supervised Federated Learning Based Intrusion Detection Method for Internet of Things," IEEE Internet of Things Journal, 2022.
[13] R. Zhao, L. Yang, Y. Wang, Z. Xue, G. Gui, and T. Ohtsuki, "A Semi-Supervised Federated Learning Scheme via Knowledge Distillation for Intrusion Detection," in ICC 2022-IEEE International Conference on Communications, 2022: IEEE, pp. 2688-2693.
[14] C. Zhang, Y. Xie, H. Bai, B. Yu, W. Li, and Y. Gao, "A survey on federated learning," Knowledge-Based Systems, vol. 216, p. 106775, 2021.
[15] L. Li, Y. Fan, M. Tse, and K.-Y. Lin, "A review of applications in federated learning," Computers & Industrial Engineering, vol. 149, p. 106854, 2020.
[16] S. I. Popoola, R. Ande, B. Adebisi, G. Gui, M. Hammoudeh, and O. Jogunola, "Federated deep learning for zero-day botnet attack detection in IoT-edge devices," IEEE Internet of Things Journal, vol. 9, no. 5, pp. 3930-3944, 2021.
[17] V. Mothukuri, R. M. Parizi, S. Pouriyeh, Y. Huang, A. Dehghantanha, and G. Srivastava, "A survey on security and privacy of federated learning," Future Generation Computer Systems, vol. 115, pp. 619-640, 2021.
[18] S. Banabilah, M. Aloqaily, E. Alsayed, N. Malik, and Y. Jararweh, "Federated learning review: Fundamentals, enabling technologies, and future applications," Information Processing & Management, vol. 59, no. 6, p. 103061, 2022.
[19] D. C. Nguyen et al., "Federated learning meets blockchain in edge computing: Opportunities and challenges," IEEE Internet of Things Journal, vol. 8, no. 16, pp. 12806-12825, 2021.
[20] W. Liang, Y. Hu, X. Zhou, Y. Pan, I. Kevin, and K. Wang, "Variational few-shot learning for microservice-oriented intrusion detection in distributed industrial IoT," IEEE Transactions on Industrial Informatics, vol. 18, no. 8, pp. 5087-5095, 2021.
[21] Z. Zhao, Y. Lai, Y. Wang, W. Jia, and H. He, "A Few-Shot Learning Based Approach to IoT Traffic Classification," IEEE Communications Letters, vol. 26, no. 3, pp. 537-541, 2021.
[22] J. Yang, Z. Zhang, and Y. Li, "Agricultural Few-Shot Selection by Model Confidences for Multimedia Internet of Things Acquisition Dataset," in 2022 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics), 2022: IEEE, pp. 488-494.
[23] W. Jia, Y. Wang, Y. Lai, H. He, and R. Yin, "FITIC: A Few-shot Learning Based IoT Traffic Classification Method," in 2022 International Conference on Computer Communications and Networks (ICCCN), 2022: IEEE, pp. 1-10.
[24] L. Yang, Y. Li, J. Wang, and N. N. Xiong, "FSLM: An intelligent few-shot learning model based on Siamese networks for IoT technology," IEEE Internet of Things Journal, vol. 8, no. 12, pp. 9717-9729, 2020.
[25] Y. Liu et al., "Fedvision: An online visual object detection platform powered by federated learning," in Proceedings of the AAAI Conference on Artificial Intelligence, 2020, vol. 34, no. 08, pp. 13172-13179.
[26] K. Dev, P. K. R. Maddikunta, T. R. Gadekallu, S. Bhattacharya, P. Hegde, and S. Singh, "Energy optimization for green communication in IoT using harris hawks optimization," IEEE Transactions on Green Communications and Networking, vol. 6, no. 2, pp. 685-694, 2022.
[27] R. Khodabakhsh, M. R. Haghifam, and M. K. Sheikh El Eslami, "Transmission system congestion management with demand-side flexibility resources considering distribution system performance improvement," Tabriz Journal of Electrical Engineering, vol. 52, no. 2, pp. 103-114, 2022, doi: 10.22034/tjee.2022.15432.
[28] Y. Darmani and M. Sangelaji, "QDFSN: QoS-enabled Dynamic and Programmable Framework for SDN," Tabriz Journal of Electrical Engineering, vol. 51, no. 1, pp. 1-10, 2021. [Online]. Available: https://tjee.tabrizu.ac.ir/article_13279_ed1fc1e794b4dc90ea1723a22dbc17b0.pdf.
[29] S. Chakraborty and K. Mazumdar, "Sustainable task offloading decision using genetic algorithm in sensor mobile edge computing," Journal of King Saud University-Computer and Information Sciences, 2022.
[30] S. Harnal, G. Sharma, N. Seth, and R. D. Mishra, "Load balancing in fog computing using qos," Energy Conservation Solutions for Fog-Edge Computing Paradigms, pp. 147-172, 2022.

Files

Share

How to cite

Statistics