## 6 Conclusions  
This work presents Fed-DTB, an adaptive federated learning framework that tackles challenges in IoV ecosystems, such as adversarial interference, inflexible trust architectures, and dependencies on centralised aggregation. Unlike prior approaches, Fed-DTB employs a dynamic trust evaluation mechanism that holistically analyses vehicular parameters (including node reliability, data consistency, and behavioural anomalies) to enable context-sensitive client selection. Experimental evaluations confirm the framework's capacity to sustain high model accuracy while filtering out malicious or unreliable participants, thereby neutralising poisoning threats and performance bottlenecks. To bridge the gap between simulation and real-world conditions, we generated an IoV dataset replicating urban mobility patterns, adversarial behaviours, and network heterogeneity. Tests across diverse scenarios reveal Fed-DTB's scalability, achieving stable convergence even in large-scale deployments. While parameter calibration for edge cases and energy-efficient adaptations necessitates deeper investigation, the framework advances secure, decentralised learning for ITS. However, three limitations qualify these gains. First, the evaluation relied on synthetic traces and a single poisoning pattern; confirming robustness on real vehicular logs and against broader adversary taxonomies (e.g., back-door or Sybil attacks) is an essential next step. Second, training latency rises noticeably as the fleet scales, which is tolerable for fleet-level analytics but unsuitable for sub-second safety loops; future work will explore roadside or hierarchical aggregation to restore real-time performance. Addressingthese points will strengthen the pathway from an experimental prototype to deployable privacy-preserving intelligence for smart city transport networks.  
Conceptualisation, A.A., S.I. and I.G.; methodology, A.A.; introduction, A.A.; results, A.A.; discussion, A.A.; formal analysis, A.A.; data curation, A.A.; conclusions, A.A.; writing--original draft preparation, A.A.; project supervision, research design, review, feedback, revision, and editing, S.I. and I.G.; supervision, S.I. and I.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The MNIST and CIFAR-10 datasets used in this study are publicly available and can be accessed via the following link: [[http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)]([http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)) (accessed on 27 March 2025), [[https://www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html)]([https://www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html)) (accessed on 27 March 2025).  
The author would like to thank [PERSON] and [PERSON] for their valuable discussions and support during this study.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} IoV & Internet of Vehicles \\ IoT & Internet of Things \\ AV & Autonomous Vehicle \\ RL & Reinforcement Learning \\ DRL & Deep Reinforcement Learning \\ FL & Federated Learning \\ Fed-DTB & Federated Learning with Dynamic Trust-Based mechanism \\ IDS & Intrusion Detection System \\ GM & Global Model \\ LM & Local Model \\ CAN & Controller Area Network \\ FedAvg & Federated Averaging \\ MNIST & Modified National Institute of Standards and Technology \\ GPU & Graphics Processing Unit \\ DSRC & Dedicated Short-Range Communications \\ AHP & Analytical Hierarchy Process \\ DP & Differential Privacy \\ ReLU & Rectified Linear Unit \\ Non-IID & Non-Independent and Identically Distributed \\ \end{tabular}