Work

A Two-Stage Hybrid Federated Learning Framework for Privacy-Preserving IoT Anomaly Detection and Classification

Year: 2025

Type: article

Abstract: The rapid surge of Artificial Internet-of-Things (AIoT) devices has outpaced the deployment of robust, privacy-preserving anomaly detection solutions suitable for resource-constrained edge environments. This paper presents a two-stage hybrid Federated Learning (FL) framework for IoT anomaly detection and classification, validated on the real-world N-BaIoT dataset. In the first stage, each device trains a generative Artificial Intelligence (AI) model on benign traffic only, and in the second stage a Histogram-based Gradient-Boosting (HGB) classifier labels flagged traffic. All models operate under a synchronous, collaborative FL architecture across nine commercial IoT devices, thus preserving data privacy and minimizing communication. Through both inter- and intra-benchmarking against state-of-the-art baselines, the Variational Autoencoder–HGB (VAE-HGB) pipeline emerges as the top performer, achieving an average end-to-end accuracy of 99.14% across all classes. These results demonstrate that reconstruction-driven generative AI models, when combined with federated averaging and efficient classification, deliver a highly scalable, accurate, and privacy-preserving solution for securing resource-constrained IoT environments.

Source: IoT

DOI: 10.3390/iot6030048

Authors: Mohammad Shahin, Ali Hosseinzadeh, F. Frank Chen

Institutions: University of Tennessee at Knoxville, The University of Texas at San Antonio

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,448