## 5 Discussion and Implications  
The FL within IoV networks has attracted much interest as it takes advantage of data privacy, improves real-time decision-making, and minimises communications overhead in large-scale vehicular environments. Despite the increasing progress of decentralised intelligence, current approaches have highly restricted applicability in real-world IoV scenarios due to their key deficiencies. Many modern approaches fall short when it comes to testing with varying adversarial threats, use static or single-factor trust mechanisms, utilise centralised cloud aggregation, and ignore the high heterogeneity and high dynamic conditions within IoV. These challenges may obstruct FL's applicability, scalability, and effectiveness in IoV networks due to the safety-critical and resource constraints. A prominent shortcoming in vehicular data-sharing schemes lies in the inadequate evaluation of adversarial attacks, such as model poisoning and inference threats. Even when certain blockchain-based FL architectures are proposed, they typically overlook the substantial computational and communication overhead introduced by consensus protocols, particularly in large-scale IoV networks, where the number of vehicles can surge exponentially. Our proposed framework, Fed-DTB, recognises the high stakes of IoV security and makes adversarial resilience a central design principle. By explicitly testing the model under various poisoning scenarios and measuring trust indicators (e.g., IDS alerts, energy usage, suspicious feedback), Fed-DTB identifies malicious participants and adapts its trust thresholds accordingly. This adaptability is crucial in preventing computational \begin{table}
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Table 3: Comparison of different FL methods. blowouts: once malicious clients are flagged and excluded, the subsequent training rounds become more efficient, thereby balancing security with system overhead. Numerous existing approaches rely on single-factor or static trust metrics, focusing solely on user feedback, IDS logs, or model performance in isolation. Such static solutions struggle to cope with dynamic IoV conditions, where network latency, node mobility, and adversarial strategies may evolve rapidly. By contrast, Fed-DTB introduces multi-factor trust evaluation, combining internal and external indicators like vehicle mobility, compliance with traffic rules, FL participation history, and real-time network stability. Moreover, the adaptive weighting mechanism within Fed-DTB reclibrates the priority of each factor based on the prevailing vehicular context. For instance, if an IoV environment is under active attack, IDS alerts are given higher weight, whereas scenarios with fluctuating connectivity might prioritise energy or network reliability metrics. This adaptability significantly enhances the framework's robustness to ever-shifting adversarial techniques and heterogeneous data conditions, bridging a core limitation of previously rigid trust approaches. Although privacy-preserving FL frameworks generally rely on secure aggregation or homomorphic encryption, many still involve centralised cloud servers that induce latency bottlenecks, especially problematic in fast-moving vehicular scenarios. Prolonged or unpredictable round times can delay model updates, compromising real-time decision-making in safety-critical use cases such as collision avoidance and traffic flow optimisations. Fed-DTB alleviates these bottlenecks by integrating client selection with an efficient, trust-driven coordination mechanism. Instead of indiscriminately waiting for all nodes to communicate with a centralised aggregator, the system focuses on high-trust vehicles that are both reliable and timely. This approach reduces round communication overhead and permits partial but higher-quality updates, mitigating latency concerns in an environment where connectivity may be transient or unstable.