## 1 Introduction  
### Background and Motivation  
The Internet of Things (IoT) has changed the world by allowing different devices to communicate and cooperate between different domains, revolutionising industries such as healthcare, smart cities, and transportation [1]. IoT technology principles motivated the development of the Internet of Vehicles (IoV) to become a promising framework that is designed to integrate smart, connected, and often autonomous vehicles into a collaborative and interactive network [2]. IoV enables advanced functionalities, including cooperative perception, predictive maintenance, and improved road safety, with real-timecommunication and collaboration among vehicles [2; 3]. Beyond facilitating inter-vehicle communication, IoV transforms vehicles into intelligent entities capable of performing a large variety of tasks independently. Using high-dimensional sensory input and sophisticated decision-making algorithms, these vehicles operate as robotic systems, adapting to dynamic environments and improving traffic flow, efficiency, and safety [4]. The complexity of IoV has led to the adoption of reinforcement learning (RL) as a promising framework that allows the vehicle to learn and make decisions through interactions with its environment [4]. Vehicles learn from their experiences, receive rewards or penalties for performing a set of actions, and adapt to optimise their behaviour over time. Since IoV is highly dimensional and complex, Deep Reinforcement Learning (DRL) extends RL's capabilities and has emerged as a promising approach for IoV, where it enables vehicles to learn adaptive strategies from high-dimensional sensory inputs and complex traffic scenarios [2; 3]. This capability of DRL improves decision-making in unpredictable and time-varying conditions, ultimately enhancing traffic flow, efficiency, and safety. However, DRL deployment in IoV faces numerous challenges, including the necessity for large, diverse, and representative datasets for effective model training in heterogeneous and dynamic environments. Moreover, it also poses challenges of privacy, security, and scalability [2; 3; 4]. To address these concerns, Federated Learning (FL) has become a promising paradigm, allowing collaborative learning among vehicles while preserving their privacy [5; 6]. In FL, vehicles train models locally on their own data and share only model updates with a central server or other vehicles. The server then aggregates these updates to form a global model, which is then distributed to the vehicles. With this process, vehicles are able to leverage collective knowledge without exposing sensitive raw data, such as driver behaviours and routes. Participation in FL gives vehicles the ability to improve object detection, understand traffic patterns, and optimise routes to improve driving performance and road safety. However, the integration of FL into IoV brings a set of significant challenges, particularly in autonomous driving systems where security and privacy requirements are critical [7]. Compromised or malicious vehicles can transmit poisoned updates containing corrupted information that aims to corrupt global model performance and introduce security vulnerability. These poisoned updates compromise the model's integrity and create delays affecting critical operations' responsiveness. Moreover, managing information flows in IoV requires robust and efficient solutions due to fluctuating communication quality, dynamic network conditions, and frequent car transitions. These interconnected challenges collectively reveal a critical research gap in current IoV federated learning approaches. While existing solutions address individual aspects such as privacy preservation or basic intrusion detection, there remains a fundamental need for integrated frameworks that can simultaneously manage trust evaluation, adapt to dynamic vehicular conditions, and maintain collaborative learning effectiveness under adversarial threats.