Federated Learning in Data Privacy and Security
Abstract Federated learning (FL) has been a rapidly growing topic in recent years. The biggest concern in federated learning is data privacy and cybersecurity. There are many algorithms that federated models have to work on to achieve greater efficiency, security, quality and effective learning. This paper focuses on algorithms such as, federated averaging algorithm, differential privacy, federated stochastic variance and reduced gradient (FSVRG). To achieve data privacy and security, this research paper presents the main data statistics with the help of graphs, visual images and design models. Later, data security in federated learning models is researched and case studies are presented to identify risks and possible solutions. Detecting security gaps is a challenge for many companies. This paper presents solutions for the identification of security-related issues which results in a decrease in time complexity and an increase in accuracy. This research sheds light on the topics of federated learning and data security.
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Zhang, J., Zhu, H., Wang, F., Zhao, J., Xu, Q., & Li, H. (2022). Security and privacy threats to federated learning: Issues, methods, and challenges. Security and Communication Networks, 2022.
Doku, R., Rawat, D. B., & Liu, C. (2019, July). Towards federated learning approach to determine data relevance in big data. In 2019 IEEE 20th international conference on information reuse and integration for data science (IRI) (pp. 184-192). IEEE.
Gosselin, R., Vieu, L., Loukil, F., & Benoit, A. (2022). Privacy and security in federated learning: A survey. Applied Sciences, 12(19), 9901.
Jatain, D., Singh, V., & Dahiya, N. (2022). A contemplative perspective on federated machine learning: Taxonomy, threats & vulnerability assessment and challenges. Journal of King Saud University-Computer and Information Sciences, 34(9), 6681-6698.
Jiang, J. C., Kantarci, B., Oktug, S., & Soyata, T. (2020b). Federated learning in smart city sensing: Challenges and opportunities. Sensors, 20(21), 6230.
Jiang, D., Shan, C., & Zhang, Z. (2020a, October). Federated learning algorithm based on knowledge distillation. In 2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE) (pp. 163-167). IEEE.
Konečný, J., McMahan, H. B., Ramage, D., & Richtárik, P. (2016). Federated optimization: Distributed machine learning for on-device intelligence. arXiv preprint arXiv:1610.02527.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE signal processing magazine, 37(3), 50-60.
McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017, April). Communicationefficient learning of deep networks from decentralized data. In Artificial intelligence and statistics (pp. 1273-1282). PMLR.
Mosaiyebzadeh, F., Pouriyeh, S., Parizi, R. M., Han, M., & Batista, D. M. (2023, May). Intrusion Detection System for IoHT Devices using Federated Learning. In IEEE INFOCOM 2023-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 1-6). IEEE.
Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., & Srivastava, G. (2021). A survey on security and privacy of federated learning. Future Generation Computer Systems, 115, 619-640.
Niknam, S., Dhillon, H. S., & Reed, J. H. (2020). Federated learning for wireless communications: Motivation, opportunities, and challenges. IEEE Communications Magazine, 58(6), 46-51.
Nilsson, A., Smith, S., Ulm, G., Gustavsson, E., & Jirstrand, M. (2018, December). A performance evaluation of federated learning algorithms. In Proceedings of the second workshop on distributed infrastructures for deep learning (pp. 1-8).
Wei, K., Li, J., Ding, M., Ma, C., Yang, H. H., Farokhi, F., ... & Poor, H. V. (2020). Federated learning with differential privacy: Algorithms and performance analysis. IEEE Transactions on Information Forensics and Security, 15, 3454-3469.
Yaacoub, J. P. A., Noura, H. N., & Salman, O. (2023). Security of federated learning with IoT systems: Issues, limitations, challenges, and solutions. Internet of Things and Cyber-Physical Systems, 3, 155-179.
Yang, F., Abedin, M. Z., & Hajek, P. (2023). An Explainable Federated Learning and Blockchain based Secure Credit Modeling Method. European Journal of Operational Research.
Yu, B., Mao, W., Lv, Y., Zhang, C., & Xie, Y. (2022). A survey on federated learning in data mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 12(1), e1443.
Zhang, K., Song, X., Zhang, C., & Yu, S. (2022). Challenges and future directions of secure federated learning: a survey. Frontiers of computer science, 16, 1-8.
Zhang, J., Zhu, H., Wang, F., Zhao, J., Xu, Q., & Li, H. (2022). Security and privacy threats to federated learning: Issues, methods, and challenges. Security and Communication Networks, 2022.
Trisha Reddy, D., Nandigam, H., Indla, S. C., & Raja, S. P. (2024). Federated Learning in Data Privacy and Security. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 13(1), e31647. https://doi.org/10.14201/adcaij.31647
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