An Efficient Unsupervised Federated Learning Approach for Anomaly Detection in Heterogeneous IoT Networks

TL;DR

This paper introduces an efficient unsupervised federated learning framework for anomaly detection in heterogeneous IoT networks, leveraging shared features and explainable AI to improve detection accuracy while preserving privacy.

Contribution

It proposes a novel unsupervised FL approach that utilizes shared features from multiple IoT datasets and employs explainable AI for better interpretability.

Findings

Significantly improves anomaly detection accuracy over traditional FL methods.

Effectively handles feature heterogeneity across diverse IoT devices.

Demonstrates robustness on real-world IoT datasets.

Abstract

Federated learning (FL) is an effective paradigm for distributed environments such as the Internet of Things (IoT), where data from diverse devices with varying functionalities remains localized while contributing to a shared global model. By eliminating the need to transmit raw data, FL inherently preserves privacy. However, the heterogeneous nature of IoT data, stemming from differences in device capabilities, data formats, and communication constraints, poses significant challenges to maintaining both global model performance and privacy. In the context of IoT-based anomaly detection, unsupervised FL offers a promising means to identify abnormal behavior without centralized data aggregation. Nevertheless, feature heterogeneity across devices complicates model training and optimization, hindering effective implementation. In this study we propose an efficient unsupervised FL framework that enhances anomaly detection by leveraging shared features from two distinct IoT datasets: one focused on anomaly detection and the other on device identification, while preserving dataset-specific features. To improve transparency and interpretability, we employ explainable AI techniques, such as SHAP, to identify key features influencing local model decisions. Experiments conducted on real-world IoT datasets demonstrate that the proposed method significantly outperforms conventional FL approaches in anomaly detection accuracy. This work underscores the potential of using shared features from complementary datasets to optimize unsupervised federated learning and achieve superior anomaly detection results in decentralized IoT environments.