Federated Learning-Based Intrusion DetectionSystem for Privacy-Preserving IoT Networks
Abstract
The rapid growth of IoT devices in smart homes, healthcare, and industrial systems has increased vulnerability to cyber threats. Traditional centralized intrusion detection systems (IDS) raise privacy, and scalability concerns due to the need for centralized data collection. This project aims to develop a Federated Learning (FL)-based IDS that enables distributed IoT nodes to collaboratively train the models without sharing raw data. This study proposes a FL-based IDS framework for IoT environments that systematically evaluates the trade-offs between detection performance, privacy guarantees, model efficiency, and synthetic zero-day like attack robustness. The framework investigates and compares multiple deep learning architectures, including Multilayer Perceptron (MLP), one-dimensional Convolutional Neural Networks (1D-CNN), and TabTransformer (TTF) architectures. The integration of differential privacy was also examined to enhance data protection and analyze its impact on model performance. Additionally, quantization techniques were investigated for the models to reduce model size and improve computational efficiency for deployment on resource-constrained IoT devices. The proposed system is evaluated using the benchmark dataset CICIoT2023 basedon standard performance metrics such as accuracy, precision, recall, F1-score, AUC-ROC score along with inference time, and communication overhead. Experimental results demonstrate that TTF achieved the highest detection performance…
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