A Novel Enhanced Approach for Security and Privacy Preserving in IoT Devices with Federal Learning Technique
Author:
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s42979-024-03104-9.pdf
Reference41 articles.
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2. Chen YC, Hsu SY, Xie X, Kumari S, Kumar S, Rodrigues J, Alzahrani BA. Privacy preserving support vector machine based on federated learning for distributed IoT-enabled data analysis. Comput Intell. 2024;40(2): e12636.
3. Wang R, Lai J, Li X, He D, Khan MK. RPIFL: Reliable and Privacy-Preserving Federated Learning for the Internet of Things. J Netw Comput Appl. 2024;221: 103768.
4. Mengistu TM, Kim T, Lin JW. A survey on heterogeneity taxonomy, security and privacy preservation in the integration of IoT, wireless sensor networks and federated learning. Sensors. 2024;24(3):968.
5. Nobakht M, Javidan R, Pourebrahimi A. SIM-FED: Secure IoT malware detection model with federated learning. Comput Electr Eng. 2024;116: 109139.
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