## 1 Introduction
### Related Works and Limitations  
Building on these challenges, previous work has proposed various approaches to enhance security, privacy, and trust management in IoV and FL systems, mostly in areas like Intrusion Detection Systems (IDS), privacy preservation, and trust evaluation. Several studies concentrated on detecting malicious activities in vehicular networks and protecting sensitive information that exists. For instance, [PERSON] and [PERSON] [8] proposed a deep learning-based IDS capable of identifying malicious messages and detecting attacks on in-vehicle protocols (e.g., CAN). Their approach improved vehicular network security by preemptively countering threats. [PERSON] et al. [9] have surveyed federated learning issues with regard to security and privacy and proposed secure aggregation and differential privacy techniques to protect data confidentiality while allowing collaborative model training. Similarly, [PERSON] et al. [10] demonstrated how deep reinforcement learning can optimise secure task offloading in IoV edge computing through trust mechanisms that boost system performance. These research studies demonstrate the importance of integrating IDS security systems with privacy improvements and strong communication capabilities throughout the IoV network [8; 9; 10; 11]. Such system measures lead to high performance while ensuring protection of sensitive data. The security and privacy methods create strong foundations, yet they might not account for performance delays resulting from poisoned updates in IoV systems. Current approaches usually optimise accuracy and privacy, but they do not take account of the overhead induced by the adversarial mitigation measures. This gap highlights the demand for latency-sensitive trust mechanisms specific to time-sensitive IoV applications. Blockchain technology has been integrated with federated learning (FL) to enhance trust and security in vehicular collaboration, offering transparency and tamper resistance for model updates. For example, [PERSON] et al. adopted blockchain technology to support hierarchical FL systems for establishing a secure data exchange between vehicles and roadside units (RSUs) in Intelligent Transportation Systems (ITS) environments [12]. Similarly, in [PERSON] et al. 's study [13], the authors built a blockchain FL system with smart contracts for protecting model update integrity, while [PERSON] et al. [14] established an FL system based on consortium blockchain to find untrustworthy participants. While the blockchain-based approaches improve transparency and help with adversarial resistance, they increase computational overhead and latency. Such oversight creates scalability problems for large-scale dynamic IoV networks because they require immediate decisions combined with fast adaptability. Blockchain technology with FL frameworks creates more trustworthy vehicular collaboration but offers restricted performance in delay-sensitive scenarios of IoV applications. In addition to security-centric methods, trust management has emerged as an essential component in IoT and FL contexts [15]. [PERSON] et al. [16] applied Analytical Hierarchy Process (AHP) to evaluate trust levels in IoT water treatment systems based on data accuracy, reliability, and system experience. [PERSON] et al. [17] demonstrated that trust-based frameworks could defend smart grid substations against cyberattacks, underscoring trust's role in critical infrastructure protection. Within federated learning, [PERSON] et al. [18] introduced a trust-based reinforcement selection approach for IoT device scheduling, incorporating trust scores and energy levels; [PERSON] and [PERSON] [19] proposed a client selection protocol in Multi-Access Edge Computing (MEC) frameworks to optimise heterogeneous resources and efficiency; and [PERSON] et al. [20] introduced a trust approach based on trust metrics to improve training accuracy and reduce initial training loss in FL servers.