A review of privacy-preserving federated learning, deep learning, and machine learning IIoT and IoTs solutions

Internet of Things (IoT) is a growing computing trend that encompasses every connected thing. Over the recent years, IoT has recorded an exponential growth, leading to billions of smart devices, and still increasing. In contrast to other computing devices, some IoTs generate large amount of data, however, this has become a source of concern as data could contain users’ privacy which should be protected at all costs against any potential security breach incident. Securing IoT is very significant with its continuous adoption and use, hence, researchers have proposed several security mechanisms and techniques to safeguard and protect IoT systems and devices. Notwithstanding, there are some research gaps that are yet to be addressed irrespective of the relevant contributions made in protection of users’ privacy and confidentiality using IoTs. In this paper, the researcher solely focused on a review of AI approaches leveraged by researchers in protecting the device and data security aspects of privacy specifically for de-centralised architecture based industrial IoT systems (IIOTs) as they are generating large amount of data and are safety critical. The results achieved, unresolved issues and recommendations for future research are contained in this review.

[1] A. Makkar, S. Garg, N. Kumar, M. S. Hossain, A. Ghoneim, and M. Alrashoud, “An efficient spam detection technique for IoT devices using machine learning,” IEEE Trans. Industr. Inform., vol. 17, no. 2, pp. 903–912, 2021.
[2] H. Mrabet, S. Belguith, A. Alhomoud, and A. Jemai, “A survey of IoT security based on a layered architecture of sensing and data analysis,” Sensors (Basel), vol. 20, no. 13, p. 3625, 2020.
[3] J. Zhang, T. Duong, R. Woods, and A. Marshall, “Securing wireless communications of the internet of things from the physical layer, an overview,” Entropy (Basel), vol. 19, no. 8, p. 420, 2017.
[4] S. Madakam, R. Ramaswamy, and S. Tripathi, “Internet of things (IoT): A literature review,” J. Comput. Commun., vol. 03, no. 05, pp. 164–173, 2015.
[5] Y. Qi, M. S. Hossain, J. Nie, and X. Li, “Privacy-preserving blockchain-based federated learning for traffic flow prediction,” Future Gener. Comput. Syst., vol. 117, pp. 328–337, 2021.
[6] C. Fang, Y. Guo, J. Ma, H. Xie, and Y. Wang, “A privacy-preserving and verifiable federated learning method based on blockchain,” Comput. Commun., vol. 186, pp. 1– 11, 2022.
[7] S. Truex et al., “A hybrid approach to privacy-preserving federated learning,”
in Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security, 2019.
[8] M. Seliem, K. Elgazzar, and K. Khalil, “Towards privacy preserving IoT environments: A survey,” Wirel. Commun. Mob. Comput., vol. 2018, pp. 1–15, 2018.
[9] C. Wohlin, “Guidelines for snowballing in systematic literature studies and a replication in software engineering,” in Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering - EASE ’14, 2014.
[10] R. C. Geyer, T. Klein, and M. Nabi, “Differentially private federated learning: A client level perspective,” arXiv [cs.CR], 2017.
[11] Z. Chen, H. Cui, E. Wu, and X. Yu, “Dynamic asynchronous anti poisoning federated deep learning with blockchain-based reputation-aware solutions,” Sensors (Basel), vol. 22, no. 2, p. 684, 2022
[12] Y. Li, C. Chen, N. Liu, H. Huang, Z. Zheng, and Q. Yan, “A blockchain-based decentralized federated learning framework with committee consensus,” IEEE Netw., vol. 35, no. 1, pp. 234–241, 2021.
[13] Y. Lu, X. Huang, Y. Dai, S. Maharjan, and Y. Zhang, “Blockchain and federated learning for privacy-preserved data sharing in industrial IoT,” IEEE Trans. Industr. Inform., vol. 16, no. 6, pp. 4177–4186, 2020.
[14] M. A. P. Chamikara et al., “Local differential privacy for federated learning,” arXiv [cs.CR], 2022.
[15] Y. Zhao et al., “Local differential privacy based federated learning for Internet of Things,” arXiv [cs.CR], 2020.
[16] Y. Liu, L. Zhang, N. Ge, and G. Li, “A systematic literature review on Federated Learning: From A model quality perspective,” arXiv [cs.LG], 2020.
[17] C. Fang, Y. Guo, Y. Hu, B. Ma, L. Feng, and A. Yin, “Privacy-preserving and communication-efficient federated learning in Internet of Things,” Comput. Secur., vol. 103, no. 102199, p. 102199, 2021.
[18] M. Hao, H. Li, G. Xu, S. Liu, and H. Yang, “Towards efficient and privacy-preserving federated deep learning,” in ICC 2019 - 2019 IEEE International Conference on Communications (ICC), 2019.
[19] Z. Liu, J. Guo, W. Yang, J. Fan, K.-Y. Lam, and J. Zhao, “Privacy-preserving aggregation in federated learning: A survey,” IEEE Trans. Big Data, pp. 1–20, 2022.
[20] J. Chen, J. Xue, Y. Wang, L. Huang, T. Baker, and Z. Zhou, “Privacy-Preserving and Traceable Federated Learning for data sharing in industrial IoT applications,” Expert Syst. Appl., vol. 213, no. 119036, p. 119036, 2023.
[21] F. Hongbin and Z. Zhi, “Privacy-preserving data aggregation scheme based on federated learning for IIoT,” Mathematics, vol. 11, no. 1, p. 214, 2023.
[22] P. C. Mahawaga Arachchige, P. Bertok, I. Khalil, D. Liu, S. Camtepe, and M. Atiquzzaman, “Local differential privacy for deep learning,” IEEE Internet Things J., vol. 7, no. 7, pp. 5827–5842, 2020.
[23] R. Hu, Y. Guo, H. Li, Q. Pei, and Y. Gong, “Personalized federated learning with differential privacy,” IEEE Internet Things J., vol. 7, no. 10, pp. 9530–9539, 2020.
[24] Y. Zhao et al., “Privacy-preserving blockchain-based federated learning for IoT devices,” IEEE Internet Things J., vol. 8, no. 3, pp. 1817–1829, 2021.
[25] X. Xiong, S. Liu, D. Li, Z. Cai, and X. Niu, “Corrigendum to ‘A comprehensive survey on local differential privacy,’” Secur. Commun. Netw., vol. 2021, pp. 1–2, 2021.
[26] M. A. P. Chamikara, P. Bertok, I. Khalil, D. Liu, and S. Camtepe, “Privacy preserving distributed machine learning with federated learning,” Comput. Commun., vol. 171, pp. 112–125, 2021.
[27] T. Zhang, T. Zhu, P. Xiong, H. Huo, Z. Tari, and W. Zhou, “Correlated differential privacy: Feature selection in machine learning,” IEEE Trans. Industr. Inform., vol. 16, no. 3, pp. 2115–2124, 2020.
[28] K. Chaudhuri and C. Monteleoni, “Privacy-preserving logistic regression,” Advances in Neural Information Processing Systems, vol. 21, 2008.
[29] J. Sengupta, S. Ruj, and S. D. Bit, “SPRITE: A scalable privacy-preserving and verifiable collaborative learning for industrial IoT,” in 2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid), 2022.
[30] P. Kumar et al., “PPSF: A privacy-preserving and secure framework using blockchain- based machine-learning for IoT-driven smart cities,” IEEE Trans. Netw. Sci. Eng., vol. 8, no. 3, pp. 2326–2341, 2021.
[31] Botchkarev, “Performance metrics (error measures) in machine learning regression, forecasting and prognostics: Properties and typology,” arXiv [stat.ME], 2018.
[32] S. Pouriyeh et al., “Secure smart communication efficiency in federated learning: Achievements and challenges,” Appl. Sci. (Basel), vol. 12, no. 18, p. 8980, 2022.
[33] C. Wang, J. Chen, Y. Yang, X. Ma, and J. Liu, “Poisoning attacks and countermeasures in intelligent networks: Status quo and prospects,” Digit. Commun. Netw., vol. 8, no. 2, pp. 225–234, 2022.
[34] M. Dibaei et al., “Attacks and defences on intelligent connected vehicles: a survey,” Digit. Commun. Netw., vol. 6, no. 4, pp. 399–421, 2020.
[35] F. Khalid, M. A. Hanif, S. Rehman, R. Ahmed, and M. Shafique, “TrISec: Training data-unaware imperceptible security attacks on deep neural networks,” in 2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS), 2019.