IoT Device Management with Federated Learning Optimization and AI
Keywords:
Federated Learning, AI optimization, IoT device management, data privacyAbstract
The need of safe and effective device management is increased for IOT devices. Decentralized machine learning approach of Federated Learning can secure and cooperate with IOT device management. The performance is improvement by Federated Learning for IOT device management is very difficult. The main objective of this paper is to find the optimized FL with AI for collaborative IOT security.
Downloads
References
McMahan, H. B., Moore, E., Ramage, D., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 1273–1282. PMLR.
Li, T., & Xu, H. (2021). Lightweight federated learning for IoT devices: Challenges and solutions. IEEE Internet of Things Journal, 8(14), 11535–11543. https://doi.org/10.1109/JIOT.2020.3036798
S. Kumari, “Kanban and AI for Efficient Digital Transformation: Optimizing Process Automation, Task Management, and Cross-Departmental Collaboration in Agile Enterprises”, Blockchain Tech. & Distributed Sys., vol. 1, no. 1, pp. 39–56, Mar. 2021
Sivaraman, Hariprasad. (2020). Integrating Large Language Models for Automated Test Case Generation in Complex Systems.
Singu, Santosh Kumar. "Real-Time Data Integration: Tools, Techniques, and Best Practices." ESP Journal of Engineering & Technology Advancements 1.1 (2021): 158-172.
S. Kumari, “Kanban-Driven Digital Transformation for Cloud-Based Platforms: Leveraging AI to Optimize Resource Allocation, Task Prioritization, and Workflow Automation”, J. of Artificial Int. Research and App., vol. 1, no. 1, pp. 568–586, Jan. 2021
S. Kumari, “Kanban and Agile for AI-Powered Product Management in Cloud-Native Platforms: Improving Workflow Efficiency Through Machine Learning-Driven Decision Support Systems”, Distrib Learn Broad Appl Sci Res, vol. 5, pp. 867–885, Aug. 2019
Singu, Santosh Kumar. "Impact of Data Warehousing on Business Intelligence and Analytics." ESP Journal of Engineering & Technology Advancements 2.2 (2022): 101-113.
Pillai, Vinayak. “Implementing Efficient Data Operations: An Innovative Approach”. Asian Journal of Multidisciplinary Research & Review, vol. 3, no. 6, Dec. 2022, pp. 231-67, https://ajmrr.org/journal/article/view/241.
S. Kumari, “Digital Transformation Frameworks for Legacy Enterprises: Integrating AI and Cloud Computing to Revolutionize Business Models and Operational Efficiency ”, Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, pp. 186–204, Jan. 2021
Sivaraman, Hariprasad. (2020). Intelligent Deployment Orchestration Using ML for Multi-Environment CI/CD Pipelines.
S. Kumari, “AI-Powered Cybersecurity in Agile Workflows: Enhancing DevSecOps in Cloud-Native Environments through Automated Threat Intelligence ”, J. Sci. Tech., vol. 1, no. 1, pp. 809–828, Dec. 2020.
S. Kumari, “Cloud Transformation and Cybersecurity: Using AI for Securing Data Migration and Optimizing Cloud Operations in Agile Environments”, J. Sci. Tech., vol. 1, no. 1, pp. 791–808, Oct. 2020.
Singu, Santosh Kumar. "Designing scalable data engineering pipelines using Azure and Databricks." ESP Journal of Engineering & Technology Advancements 1.2 (2021): 176-187.
Sivaraman, Hariprasad. (2021). INTELLIGENT AUTOMATION FOR SERVICE DEGRADATION PREDICTION USING LLMS AND OBSERVABILITY DATA. International Journal of Engineering Management. 6. 10.5281/zenodo.14342920.
S. Kumari, “AI-Powered Cloud Security for Agile Transformation: Leveraging Machine Learning for Threat Detection and Automated Incident Response ”, Distrib Learn Broad Appl Sci Res, vol. 6, pp. 467–488, Oct. 2020
Singu, Santosh Kumar. "ETL Process Automation: Tools and Techniques." ESP Journal of Engineering & Technology Advancements 2.1 (2022): 74-85.
Zhang, S., & Zhou, T. (2021). Blockchain-based verification and federated learning for secure IoT systems. IEEE Transactions on Industrial Informatics, 17(3), 2124–2133. https://doi.org/10.1109/TII.2020.2973085
Li, X., & Yang, Q. (2019). A survey of federated learning: Techniques, applications, and challenges. IEEE Transactions on Knowledge and Data Engineering, 31(9), 1637–1652. https://doi.org/10.1109/TKDE.2018.2844958
Liu, Y., & Zhou, W. (2020). Machine learning and optimization for federated learning. IEEE Transactions on Neural Networks and Learning Systems, 31(4), 1109–1120. https://doi.org/10.1109/TNNLS.2019.2925941
Kairouz, P., McMahan, H. B., & Xia, X. (2020). Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1), 1–210. https://doi.org/10.1561/2200000083
Acar, A., & Khandelwal, M. (2021). Security issues in federated learning. ACM Computing Surveys (CSUR), 54(10), 1–27. https://doi.org/10.1145/3403524
Shokri, R., & Shmatikov, V. (2015). Privacy-preserving deep learning. Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, 1310–1321. https://doi.org/10.1145/2810103.2813687
Xu, S., & Zhang, H. (2020). Privacy-preserving federated learning: Threat models and countermeasures. IEEE Transactions on Dependable and Secure Computing, 17(3), 455–467. https://doi.org/10.1109/TDSC.2019.2909005
Yang, Z., & Liu, Z. (2021). Secure federated learning for collaborative IoT. Proceedings of the IEEE International Conference on Communications (ICC), 1–6. https://doi.org/10.1109/ICC42927.2021.9502798
Liang, C., & Zhang, Y. (2020). A survey on optimization techniques for federated learning. IEEE Transactions on Neural Networks and Learning Systems, 31(4), 1109–1120. https://doi.org/10.1109/TNNLS.2020.2981737
Wei, Y., & Li, B. (2021). Edge computing and federated learning for IoT. IEEE Internet of Things Journal, 8(12), 9470–9481. https://doi.org/10.1109/JIOT.2021.3052652
Raji, I. D., & Buolamwini, J. (2021). Ethical challenges in AI and federated learning. AI and Ethics, 1(1), 1–12. https://doi.org/10.1007/s43681-021-00003-2
Madaan, P., & Singh, A. (2020). Federated learning for IoT: Privacy and security challenges. Future Generation Computer Systems, 108, 526–537. https://doi.org/10.1016/j.future.2020.03.039