Optimizing Heterogeneity in IoT Infra Using Federated Learning and Blockchain-based Security Strategies
DOI:
https://doi.org/10.15837/ijccc.2023.6.5890Keywords:
Security, Heterogeneity, Success Rate, Vulnerability, Federated Learning, performance, A-GAN, AccuracyAbstract
The Internet of Things (IoT) and associated capabilities are becoming indispensable in the planning, operation, and administration of intricate systems of all sizes. High-end learning solutions that go beyond the boundaries of the problem are necessary for addressing the variety of communication concerns (compatibility, secure communication, etc.) in IoT settings. Building machine learning (ML) networks from disparate data sources is a cutting-edge practice known as Federated Learning (FL). In this article, we implement FL between edge-based servers and devices in a sparsely populated cloud to facilitate cohesive learning and the storage of critical information in smart IoT systems. FL enables collaborative training from a common model by aggregating smaller unit models via regulated edge network participants. Further, all the susceptible device’s information and sensitive message transactions are addressed via blockchain technology. Thus, a blockchain-based security mechanism is integrated to secure user privacy and facilitate widespread practical adoption. Finally, a comparison is made between the proposed model and the three best free, open-source Federated Learning models already in use (FedPD, FedProx, and FedAvg). In terms of statistical, and data heterogeneity (>70% SDI, >97% accuracy), the experimental findings suggest that the proposed model performs better than the existing techniques.References
Rajagopal A, Nirmala V. Federated AI lets a team imagine together: federated learning of GANs. Int. J. Comput. Sci. Eng. 2019;7(5):704-9.
https://doi.org/10.26438/ijcse/v7i5.704709
Banos O, Garcia R, Holgado-Terriza JA, Damas M, Pomares H, Rojas I, Saez A, Villalonga C. mHealthDroid: a novel framework for agile development of mobile health applications. InAmbient Assisted Living and Daily Activities: 6th International Work-Conference, IWAAL 2014, Belfast, UK, December 2-5, 2014. Proceedings 6 2014 (pp. 91-98). Springer International Publishing.
https://doi.org/10.1007/978-3-319-13105-4_14
Banos O, Villalonga C, Garcia R, Saez A, Damas M, Holgado-Terriza JA, Lee S, Pomares H, Rojas I. Design, implementation and validation of a novel open framework for agile development of mobile health applications. Biomedical engineering online. 2015 Dec;14(2):1-20.
https://doi.org/10.1186/1475-925X-14-S2-S6
Deng S, Zhao H, Fang W, Yin J, Dustdar S, Zomaya AY. Edge intelligence: The confluence of edge computing and artificial intelligence. IEEE Internet of Things Journal. 2020 Apr 1;7(8):7457-69.
https://doi.org/10.1109/JIOT.2020.2984887
Dillon T, Wu C, Chang E. Cloud computing: issues and challenges. In2010 24th IEEE international conference on advanced information networking and applications 2010 Apr 20 (pp. 27-33). Ieee.
https://doi.org/10.1109/AINA.2010.187
Hassan N, Yau KL, Wu C. Edge computing in 5G: A review. IEEE Access. 2019 Aug 30;7:127276- 89.
https://doi.org/10.1109/ACCESS.2019.2938534
Ahmed, S. T., Kumar, V., & Kim, J. (2023). AITel: eHealth Augmented Intelligence based Telemedicine Resource Recommendation Framework for IoT devices in Smart cities. IEEE Internet of Things Journal..
https://doi.org/10.1109/JIOT.2023.3243784
Hu B, Fang Y, Shi C. Adversarial learning on heterogeneous information networks. InProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2019 Jul 25 (pp. 120-129).
https://doi.org/10.1145/3292500.3330970
Johnson AE, Pollard TJ, Shen L, Lehman LW, Feng M, Ghassemi M, Moody B, Szolovits P, Anthony Celi L, Mark RG. MIMIC-III, a freely accessible critical care database. Scientific data. 2016 May 24;3(1):1-9.
https://doi.org/10.1038/sdata.2016.35
Kairouz P, McMahan HB, Avent B, Bellet A, Bennis M, Bhagoji AN, Bonawitz K, Charles Z, Cormode G, Cummings R, D'Oliveira RG. Advances and open problems in federated learning. Foundations and Trends® in Machine Learning. 2021 Jun 22;14(1-2):1-210.
https://doi.org/10.1561/2200000083
Karimireddy SP, Kale S, Mohri M, Reddi S, Stich S, Suresh AT. Scaffold: Stochastic controlled averaging for federated learning. InInternational Conference on Machine Learning 2020 Nov 21 (pp. 5132-5143). PMLR.
K. Gunasekaran, V. V. Kumar, A. C. Kaladevi, T. R. Mahesh, C. R. Bhat and K. Venkatesan, "Smart Decision-Making and Communication Strategy in Industrial Internet of Things," in IEEE Access, vol. 11, pp. 28222-28235, 2023.
https://doi.org/10.1109/ACCESS.2023.3258407
Li T, Sahu AK, Zaheer M, Sanjabi M, Talwalkar A, Smith V. Federated optimization in heterogeneous networks. Proceedings of Machine learning and systems. 2020 Mar 15;2:429-50.
Li T, Sanjabi M, Beirami A, Smith V. Fair resource allocation in federated learning. arXiv preprint arXiv:1905.10497. 2019 May 25.
KarthickRaghunath KM, Koti MS, Sivakami R, Vinoth Kumar V, NagaJyothi G, Muthukumaran V. Utilization of IoT-assisted computational strategies in wireless sensor networks for smart infrastructure management. International Journal of System Assurance Engineering and Management. 2022 Jan 30:1-7.
https://doi.org/10.1007/s13198-021-01585-y
Ro J, Chen M, Mathews R, Mohri M, Suresh AT. Communication-efficient agnostic federated averaging. arXiv preprint arXiv:2104.02748. 2021 Apr 6.
https://doi.org/10.21437/Interspeech.2021-153
Venkatesan, Vinoth Kumar, Ivan Izonin, JayalakshmiPeriyasamy, AlagiriIndirajithu, Anatoliy- Batyuk, and Mahesh Thyluru Ramakrishna. "Incorporation of Energy Efficient Computational Strategies for Clustering and Routing in Heter-ogeneous Networks of Smart City." Energies 15, no. 20 (2022): 7524
https://doi.org/10.3390/en15207524
Tarcar AK. Advancing Healthcare Solutions with Federated Learning. InFederated Learning: A Comprehensive Overview of Methods and Applications 2022 Feb 8 (pp. 499-508). Cham: Springer International Publishing.
https://doi.org/10.1007/978-3-030-96896-0_23
Thapa C, Chamikara MA, Camtepe SA. Advancements of federated learning towards privacy preservation: from federated learning to split learning. Federated Learning Systems: Towards Next-Generation AI. 2021:79-109.
https://doi.org/10.1007/978-3-030-70604-3_4
Wang S. Edge computing: applications, state-of-the-art and challenges. Advances in Networks. 2019 Nov 15;7(1):8-15.
https://doi.org/10.11648/j.net.20190701.12
Kumar, V. Vinoth, V. Muthukumaran, N. Ashwini, I. S. Beschi, K. Gunasekaran, and V. R. Niveditha. "An efficient sign-cryption scheme using near-ring hybrid approach for an IoT-based system." International Journal of e-Collaboration (IJeC) 18, no. 1 (2022): 1-31.
https://doi.org/10.4018/IJeC.299011
Zhang X, Hong M, Dhople S, Yin W, Liu Y. FedPD: A federated learning framework with adaptivity to non-iid data. IEEE Transactions on Signal Processing. 2021 Oct 1;69:6055-70.
https://doi.org/10.1109/TSP.2021.3115952
Zhou P, Lin Q, Loghin D, Ooi BC, Wu Y, Yu H. Communication-efficient decentralized machine learning over heterogeneous networks. In2021 IEEE 37th International Conference on Data Engineering (ICDE) 2021 Apr 19 (pp. 384-395). IEEE.
https://doi.org/10.1109/ICDE51399.2021.00040
Muthukumaran, V., Vinoth Kumar, V., Joseph, R. B., Munirathnam, M., Beschi, I. S., & Niveditha, V. R. (2022, November). Efficient Authenticated Key Agreement Protocol for Cloud- Based Internet of Things. In International Conference on Innovative Computing and Communications: Proceedings of ICICC 2022, Volume 3 (pp. 365-373). Singapore: Springer Nature Singapore.
https://doi.org/10.1007/978-981-19-3679-1_28
Jatain, D., Singh, V., &Dahiya, N. (2021). A Contemplative Perspective on Federated Machine Learning: Taxonomy, Threats & Vulnerability Assessment and Challenges. Journal of King Saud University - Computer and Information Sciences.
https://doi.org/10.1016/j.jksuci.2021.05.016
Qureshi, J. N., Farooq, M. S., Abid, A., Umer, T., Bashir, A. K., &Zikria, Y. B. (2022). Blockchain applications for the Internet of Things: Systematic review and challenges. Microprocessors and Microsystems, 94, 104632.
https://doi.org/10.1016/j.micpro.2022.104632
Jatain, D., Singh, V., &Dahiya, N. (2022). Blockchain Base Community Cluster-Federated Learning for Secure Aggregation of Healthcare Data. Procedia Computer Science, 215, 752-762.
https://doi.org/10.1016/j.procs.2022.12.077
Mallah, R. A., López, D., & Halabi, T. (2023). Blockchain-enabled Efficient and Secure Federated Learning in IoT and Edge Computing Networks. 2023 International Conference on Computing, Networking and Communications (ICNC).
https://doi.org/10.1109/ICNC57223.2023.10074277
Waheed, N., & Usman, M. (2020). Security and Privacy in IoT Using Machine Learning and Blockchain: Threats and Countermeasures. Corpus ID: 221137844
https://doi.org/10.1145/3417987
Lu, Y., Huang, X., Zhang, K., Maharjan, S., & Zhang, Y. (2021). Communication-Efficient Federated Learning and Permissioned Blockchain for Digital Twin Edge Networks. IEEE Internet of Things Journal, 8(4), 2276-2288.
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