Adaptive federated learning scheme for recognition of malicious attacks in an IoT network

Computing - Trang 1-16 - 2023
Prateek Chhikara1, Rajkumar Tekchandani1, Neeraj Kumar1,2,3,4
1Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, India
2Department of Electrical and Computer Engineering , Lebanese American University, Beirut, Lebanon
3School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
4Faculty of Computing and IT, King Abdulaziz University, Jeddah, Saudi Arabia

Tóm tắt

The Internet of Things (IoT) is crucial for deploying a novel Artificial Intelligence (AI) model for both network and application management. However, using classical centralized learning algorithms in the IoT environment is challenging, given massively distributed private datasets. Advancements in AI have helped us solve various use cases, but it operates under two significant challenges. Firstly, the data exists in separate clusters, and secondly, the current AI has limited data privacy and security. Federated learning (FL) aims to preserve data privacy through distributed learning methods that keep the data in storage silos. Likewise, differential privacy improves data privacy by measuring the privacy loss in communication among the elements of FL. The paper proposes two adaptive approaches for making model training differentially private in a vertical federated environment. The first one uses random feature selection to train different machine learning models, and performance improvement is also proposed. The second approach uses a tree structure, i.e., Classification and Regression Trees, using some defined constraints. Further, we created a scheme to help identify malicious users/devices in a federated network cluster using parity checks for every FL iteration.

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