This paper aims to propose a novel framework to address the data privacy issue for Federated Learning (FL)-based Intrusion Detection Systems (IDSs) in Internet-of-Vehicles(IoVs) with limited computational resources. In particular, in conventional FL systems, it is usually assumed that the computing nodes have sufficient computational resources to process the training tasks. However, in practical IoV systems, vehicles usually have limited computational resources to process intensive training tasks, compromising the effectiveness of deploying FL in IDSs. While offloading data from vehicles to the cloud can mitigate this issue, it introduces significant privacy concerns for vehicle users (VUs). To resolve this issue, we first propose a highly-effective framework using homomorphic encryption to secure data that requires offloading to a centralized server for processing. Furthermore, we develop an effective training algorithm tailored to handle the challenges of FL-based systems with encrypted data. This algorithm allows the centralized server to directly compute on quantum-secure encrypted ciphertexts without needing decryption. This approach not only safeguards data privacy during the offloading process from VUs to the centralized server but also enhances the efficiency of utilizing FL for IDSs in IoV systems. Our simulation results show that our proposed approach can achieve a performance that is as close to that of the solution without encryption, with a gap of less than 0.8%.

翻译：本文旨在提出一种新颖框架，以解决资源受限车联网中基于联邦学习的入侵检测系统所面临的数据隐私问题。传统联邦学习系统通常假设计算节点拥有充足的计算资源来处理训练任务，然而在实际车联网系统中，车辆通常仅具备有限的计算资源，难以处理密集型训练任务，这影响了联邦学习在入侵检测系统中部署的有效性。虽然将车辆数据卸载至云端可缓解此问题，但这会引发车辆用户的重大隐私担忧。为解决该问题，我们首先提出一种高效框架，利用同态加密技术保护需要卸载至集中式服务器进行处理的数据安全。此外，我们开发了一种专门应对加密数据联邦学习系统挑战的高效训练算法。该算法使集中式服务器能够直接在量子安全的加密密文上进行计算，无需解密操作。该方法不仅保障了从车辆用户到集中式服务器的数据卸载过程中的隐私安全，同时提升了联邦学习在车联网入侵检测系统中的运用效率。仿真结果表明，所提方案性能与未加密方案的差距小于0.8%，几乎达到同等效能水平。