## 5 Discussion and Implications
Another barrier to IoV-centric FL research is the lack of public datasets representing realistic adversarial behaviours, diverse network topologies, and resource constraints. Many published benchmarks either simulate homogeneous traffic or omit malicious client scenarios altogether, rendering them less applicable to real-world vehicular contexts. Recognising this, Fed-DTB includes a custom synthetic dataset that captures adversarial attacks, dynamic vehicular states, and energy fluctuations. By incorporating these diverse conditions, the experiments more faithfully replicate the environment of a smart city or complex ITS deployment. Moreover, the results presented (including performance metrics in different client scale configurations) offer valuable scalability insights that many existing FL frameworks do not provide. Such analyses underscore the model's capacity to adapt across small- to medium-scale IoV networks and pave the way for larger-scale adoption. Resource allocation and scheduling in IoV-FL lean on advanced algorithms such as DRL. However, untested learning latency and incomplete privacy simulations erode confidence in their operational viability. More critically, these approaches often assume fixed adversarial conditions or ignore the overhead of trust evaluations. In contrast, Fed-DTB embraces context-aware resource management: the trust-driven client selection process naturally accommodates the dynamic availability of computing and communication resources. By prioritising resource-limited but high-trust vehicles, the framework ensures minimal training disruption and fosters more reliable convergence in scenarios with shifting vehicular populations or intermittent power constraints. Moreover, real-time performance is bolstered by the early exclusion of untrustworthy nodes; rather than expending cycles on suspicious updates, Fed-DTB swiftly channels resources towards genuinely contributing clients. This strategy addresses a core shortcoming in existing DRL-based resource schedulers, where slow or inconclusive trust checks can hamper real-time responsiveness. Finally, verifying the convergence behaviour of FL models in non-IID vehicular networks remains an unresolved question in much of the literature. The ephemeral and mobile nature of vehicles, combined with adversarial data injection, raises doubts about whether global models can stabilise within practical round limits. The Fed-DTB multi-factor trust approach tackles this by filtering out outlier contributions early in each round, preserving the integrity of global updates. Empirical findings indicate that convergence can be achieved consistently despite widely varying data distributions and malicious interference. The Fed-DTB remains explicable because every trust decision is the weighted sum of seven observable factors, and those weights are logged in each round. Operators can trace, for instance, how the IDS weight dominates during an attack and recedes once the threat is cleared. Preliminary counts of client selection in ten random seeds yield a coefficient of variation below 0.25, indicating that no benign subgroup is systematically excluded. These properties suggest that the framework offers transparent reasoning while treating participants evenhandedly. By systematically addressing the core limitations of current FL solutions for IoV adversarial threats, static trust mechanisms, scalability bottlenecks, and the lack of realistic vehicular datasets, Fed-DTB presents a holistic design that moves the field towards more robust, context-aware deployments. Its adversarial resilience stems from active detection and the rapid exclusion of poisoned clients via multi-factor trust checks, ensuring compromised updates do not linger in the global model. Meanwhile, the adaptive trust mechanism employs a context-aware weighting strategy that dynamically reconfigures the focus of trust factors (e.g., IDS alerts, energy usage, or network stability) in response to evolving vehicular conditions.