为车联网设计轻量级联邦学习入侵检测框架，提升安全性和部署效率。
Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT
- 中心服务器预训练+边缘设备轻量微调，降低资源消耗。
- 内存占用减少42%，训练时间缩短75%，准确率达99.2%。
- 适合资源受限的车载物联网场景，可扩展性强。
机器学习与边缘计算的快速发展推动了车联网（CAV）和智能交通系统（ITS）的发展，提升了交通管理与车辆安全水平，但也带来了网络安全与隐私泄露风险。传统入侵检测系统（IDS）难以应对现代威胁，促使基于机器学习的解决方案兴起。联邦学习（FL）通过在分布式边缘设备上进行去中心化模型训练，避免敏感数据共享，成为可行方案。然而，在车联网中部署FL-based IDS面临诸多挑战：边缘设备算力与内存有限，需兼顾导航、安全等关键应用需求，且硬件与网络条件差异大。为此，本文提出一种混合式服务器-边缘联邦学习框架，将预训练任务移至中心服务器，边缘设备仅执行轻量级微调。该方法可减少42%内存使用，训练时间降低75%，并实现最高99.2%的检测准确率。可扩展性分析表明，随着客户端数量增加，性能下降极小，验证了其在车联网及其他物联网应用中的可行性。
原文摘要 · Abstract (English)
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.
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