Privacy‑Enhanced Federated Learning Framework for Intrusion Detection in Smart IoT Environments

Himanshu Sharma; Ashok  Kumar; Gautam  Kumar

doi:10.65890/race.v1i2.153

Authors

Himanshu Sharma Department of CSE, SUSCSE, Sharda University, Greater Noida, India Author
Ashok Kumar Department of IT, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India Author
Gautam Kumar Department of Electrical, Electronics and Communication Engineering, School of Engineering, Galgotias University, Greater Noida, India. Author

DOI:

https://doi.org/10.65890/race.v1i2.153

Keywords:

Federated Learning, IDS, IoT Security, Smart Devices, Privacy preserving

Abstract

The rapid proliferation of the Internet of Things (IoT) has given rise to Smart Environments where sensors, actuators and embedded systems interact seamlessly to provide automation and convenience. Recent reports estimate that the number of connected IoT devices reached 14.4 billion in 2022 and continues to grow despite supply‑chain disruptions. Unfortunately, this connectivity also exposes critical infrastructures to malware, botnets and other cyber‑attacks. Conventional intrusion detection systems (IDS) are often ill‑suited for resource‑constrained IoT nodes because they require centralised data collection, violating privacy regulations and incurring excessive bandwidth consumption. Federated learning (FL) has emerged as a promising paradigm to overcome these limitations by enabling collaborative model training directly on edge devices. However, FL alone does not guarantee privacy, model updates may leak sensitive information and naive aggregators remain vulnerable to single points of failure. This work proposes a privacy‑enhanced federated learning framework for anomaly and malware detection in Smart IoT environments. The contributions of this research paper is threefold: (1) a hierarchical FL architecture that distributes computation across edge, fog and cloud tiers, incorporating differentially private noise to model updates is designed; (2) a multi‑agent intrusion detection algorithm that trains lightweight deep models locally using real traffic data while a fog‑level coordinator performs secure aggregation is developed; and (3) extensive experiments on modern IoT intrusion datasets to evaluate detection accuracy, communication overhead and resource consumption is done. The results show that the proposed framework achieves comparable accuracy to centralised training while substantially improving privacy and resilience.

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