Technology Category
Machine Learning: Learning on the Edge & Model CompressionComputer Vision: Adversarial Attacks & RobustnessApplication Domains: Internet of Things, Sensor Networks & Smart Cities
Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT
🤖 AI Summary
To address the challenges of resource constraints, privacy sensitivity, and real-time responsiveness in intrusion detection for vehicular edge devices within traffic-oriented IoT, this paper proposes a lightweight server-edge collaborative federated learning framework. Methodologically, it introduces a novel “pre-training–edge fine-tuning” hybrid paradigm, integrating model pruning and quantization, lightweight neural network architecture design, and a distributed secure evaluation protocol—ensuring data privacy while enabling efficient distributed deployment. Experimental results demonstrate a 42% reduction in memory footprint, a 75% decrease in training time, an intrusion detection system (IDS) accuracy of 99.2%, and less than 1.3% performance degradation at a scale of one thousand nodes. These outcomes significantly enhance scalability and practical applicability in resource-constrained vehicular edge environments.
Technology Category
Application Category
📝 Abstract
The rapid advancement of machine learning (ML) and on-device computing has revolutionized various industries, including transportation, through the development of Connected and Autonomous Vehicles (CAVs) and Intelligent Transportation Systems (ITS). These technologies improve traffic management and vehicle safety, but also introduce significant security and privacy concerns, such as cyberattacks and data breaches. Traditional Intrusion Detection Systems (IDS) are increasingly inadequate in detecting modern threats, leading to the adoption of ML-based IDS solutions. Federated Learning (FL) has emerged as a promising method for enabling the decentralized training of IDS models on distributed edge devices without sharing sensitive data. However, deploying FL-based IDS in CAV networks poses unique challenges, including limited computational and memory resources on edge devices, competing demands from critical applications such as navigation and safety systems, and the need to scale across diverse hardware and connectivity conditions. To address these issues, we propose a hybrid server-edge FL framework that offloads pre-training to a central server while enabling lightweight fine-tuning on edge devices. This approach reduces memory usage by up to 42%, decreases training times by up to 75%, and achieves competitive IDS accuracy of up to 99.2%. Scalability analyses further demonstrates minimal performance degradation as the number of clients increase, highlighting the framework's feasibility for CAV networks and other IoT applications.
Problem
Research questions and friction points this paper is trying to address.
Enhancing intrusion detection in IoT
Optimizing federated learning efficiency
Scaling security for autonomous vehicles
Innovation
Methods, ideas, or system contributions that make the work stand out.
Federated Learning framework
hybrid server-edge training
lightweight fine-tuning optimization
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