  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%.
