## 1 Introduction
### Related Works and Limitations
While they show that trust brings benefits to the efficiency and security of IoT and FL networks, they fail to address the key challenges concerning IoV environments. Recent advances in FL-IoT research have focused on secure data management and resource optimisation in vehicular networks. [PERSON] et al. [21] established a taxonomy of FL-IoT services, highlighting the role of federated learning in the management of secure vehicular data. Subsequent studies expand FL to address resource optimisation and scalability challenges. For instance, FL optimises vehicular data offloading via RSU-mediatedresource allocation [22], while vertical and horizontal FL architectures [23; 24] employ differential privacy [25] and cloud aggregation to safeguard sensitive data. However, these frameworks lack rigorous evaluations of resilience against adversarial attacks or inference threats. Privacy-preserving FL models [23; 24] also incur high communication latency due to centralised cloud aggregation, limiting real-time applicability. Furthermore, data offloading strategies [22] fail to address privacy risks when third-party aggregators process sensitive information. FL-driven resource management strategies aim to enhance efficiency in vehicular systems. Centralised federated learning (CFL) optimises power control [26], while DRL-based schemes improve resource allocation in vehicle-to-everything (V2X) communication [27]. Decentralised FL (DFL) further refines efficiency in MEC-aided V2V networks [28]. Beyond transportation, FL supports smart city applications by leveraging vehicles as clients for city-scale data management [29]. Despite these innovations, resource management models [26; 27; 28] exhibit untested learning latency and unverified accuracy in DRL-based methods, which may compromise real-time ITS and V2X performance. Privacy simulations in these models remain incomplete, and smart city FL implementations [29] lack scalability analyses, raising concerns about adaptability to large-scale deployments. Recent advances in federated learning security have demonstrated significant progress in addressing both privacy and robustness challenges, with machine learning approaches showing increasing effectiveness in cybersecurity applications [30; 31]. [PERSON] et al. [32] proposed a novel privacy-preserving aggregation framework that protects against inference attacks using computational Diffie-Hellman (CDH) encryption, achieving minimal accuracy loss while maintaining lightweight computation costs. Similarly, [PERSON] et al. [33] introduced FedSuper, a Byzantine-robust federated learning approach that utilises shadow dataset supervision to maintain robustness against threatening scenarios with high Byzantine ratios (up to 0.9) and extreme non-IID data distributions. While these advances address crucial privacy preservation and Byzantine robustness challenges in general federated learning contexts, the Internet of Vehicles domain presents unique operational requirements that necessitate specialised approaches. IoV environments exhibit distinct characteristics, including real-time mobility constraints, dynamic energy limitations, fluctuating network topologies, and safety-critical decision requirements [34] that are not fully addressed by existing privacy or robustness frameworks. This motivates the development of IoV-specific trust mechanisms that can dynamically adapt to vehicular context while ensuring both security and operational efficiency. Across these lines of work, we observe three persistent tensions: (i) security-centric schemes seldom quantify resource cost and latency; (ii) resource-optimisation studies treat trust as static or implicit, overlooking adversarial behaviour; (iii) single-factor trust approaches ignore mobility-induced drift and heterogeneous data. Fed-DTB resolves these tensions by unifying dynamic multi-factor trust with cost-aware optimisation and by validating the design under simulated vehicular mobility and adversarial conditions.