ItemConference Paper

Federated Learning for Privacy-Preserving Threat Detection in IoT-Enabled Networks

Title
Federated Learning for Privacy-Preserving Threat Detection in IoT-Enabled Networks
Creator
Logeshwaran, J.; Dhanasekaran, S.; Chandra Sati, Dayal; Sama, Mukhtar; Garg, Amit; Singhal, Saurabh
Description
The advent of Internet of Things (IoT) devices at a continuous rapid pace has greatly increased the surface for cyberattacks to measure the effectiveness of threat detection mechanisms. Most conventional centralized threat detection frameworks require sending sensitive device data to a single central server for aggregation, with significant privacy risks and scalability challenges. Such challenges could be efficiently addressed with the use of Federated Learning (FL), an emerging decentralized paradigm of training machine learning models, through the collaboration of a large number of devices, such as IoT sensors, that store the data locally and do not share raw data. In this work, we integrate FL to propose a threat detection framework for preserving privacy in IoT-enabled networks. In this paper, we propose a system architecture in which edge devices perform local training of machine learning models on encrypted traffic and behavioral data and then periodically share only the model updates with a centralized aggregator. This approach ensures the privacy of the data, minimizes communication overhead, and improves detection capabilities for real-time threats. The efficacy of FL-based threat detection is examined through experimental evaluations on benchmark datasets of IoT attack traces, indicating that FL-based approaches achieve competitive accuracy versus prior centralized schemes while greatly mitigating risks of data leakage. We further address issues regarding heterogeneous device resources, communication efficiency, and adversarial attack resilience in this context. Our results indicate that federated learning is a very effective approach for providing IoT environment protection, as it securely balances privacy, scalability, and detection performance. 2025 IEEE.
Source
Proceedings - 2025 IEEE 1st International Conference on Smart Innovations in Systems, Infrastructure, Mechanical, Power, AI and Computing Technologies, SISIMPACT 2025;pp.1005-1010
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Adversarial; Architecture; Conventional; Heterogeneous; Scalability
Coverage
Logeshwaran J., Christ University, Department of Computer Science, Karnataka, Bengaluru, India; Dhanasekaran S., Sri Eshwar College of Engineering, Department of Electronics and Communication Engineering, Tamilnadu, Coimbatore, India; Chandra Sati D., Apex Institute of Technology, Chandigarh University, Gharun, India; Sama M., Marwadi University, Department of Mechanical Engineering, Gujarat, Rajkot, India; Garg A., Manipal University Jaipur, Department of Computer Science and Engineering, Rajasthan, Jaipur, India; Singhal S., Apex Institute of Technology, Chandigarh University, Department of CSE, Chandigarh, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833155787-4;
Format
online
Language
English
Type
Conference paper

Citation

Logeshwaran, J.; Dhanasekaran, S.; Chandra Sati, Dayal; Sama, Mukhtar; Garg, Amit; Singhal, Saurabh, “Federated Learning for Privacy-Preserving Threat Detection in IoT-Enabled Networks,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26211.

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