## 3 Proposed Framework (Fed-DTB)
### Trust Management System
#### 3.1.1 Trust Score Application  
As introduced in Section 2, each vehicle \(v_{i}\) calculates an overall trust score \(\tau_{i}(t)\) at each time step \(t\). The trust score integrates seven carefully selected factors, each normalised to a value between 0 and 1, where 0 indicates completely untrustworthy behaviour and 1 represents fully trustworthy conduct. Trust Factor Selection Rationale and Operational Feasibility  
The selection of our seven-factor trust evaluation framework is systematically grounded in established IoV operational requirements, security standards, and practical deployment considerations. Each factor addresses specific challenges in vehicular federated learning while utilising data readily available through existing vehicular infrastructure:  
* **IDS Alert Score (\(\tau_{I_{i}}(t)\)):** Serves as the primary security indicator mandated by connected vehicle cybersecurity standards (ISO/SAE 21434) [46]. Indicates whether IDS has detected suspicious or malicious activity from \(v_{i}\). Available through onboard intrusion detection systems. IDS alerts are critical; if triggered, they can nullify the entire trust score, ensuring that vehicles suspected of malicious intent are excluded from FL participation. * **Network Status Score (\(\tau_{N_{i}}(t)\)):** Essential for V2X communication reliability, directly impacting the quality and timeliness of model parameter exchanges. Reflects the stability and reliability of the vehicle's network connection, as IoV networks are susceptible to varying signal strengths, bandwidth limitations, and intermittent connectivity. Obtained via standard V2X communication modules (DSRC/C-V2X) already deployed in connected vehicles for safety applications. * **Vehicle Status Score (\(\tau_{V_{S}}(t)\)):** Determines the computational reliability and sustained participation capability. Evaluates the operational health of the vehicle's components (e.g., engine, brakes, sensors), as a well-maintained and reliable vehicle is more likely to produce accurate and trustworthy data. Accessible through OBD-II diagnostic ports mandated in all vehicles manufactured after 1996. * **Energy Efficiency Score (\(\tau_{E_{i}}(t)\)):** Ensures sustainable long-term participation in resource-constrained vehicular environments. Measures how efficiently a vehicle manages its energy resources, as vehicles that risk running low on energy may fail to complete their local training or share timely updates, reducing their reliability. Provided by existing battery management systems and fuel monitoring systems accessible through CAN bus interfaces. * **Compliance Score (\(\tau_{C_{i}}(t)\)):** Addresses regulatory requirements and safety assurance, increasingly important for autonomous vehicle operations. Assesses traffic rules and regulations adherence, as non-compliant vehicles may engage in risky behaviours that contaminate data quality or introduce unusual patterns into the learning process. Compliance with traffic regulations is evaluated through onboard telemetry data sourced from vehicular control systems and GPS navigation systems already present in modern vehicles. * **Federated Learning Participation Score (\(\tau_{P_{i}}(t)\)):** Measures collaborative reliability and commitment to the federated learning process through historical participation patterns. Quantifies the vehicle's contribution to the FL process, considering factors like the volume and quality of its local data and the magnitude of its model updates. More substantial and useful contributions raise a vehicle's trustworthiness. Maintained by FL infrastructure logs as part of standard protocol operations. * **User Feedback Score (\(\tau_{U_{i}}(t)\)):** Captures real-world operational assessment and behavioural patterns from vehicle occupants. Incorporates community or user-based feedback regarding the vehicle's reliability, performance, or user satisfaction levels, thereby adding a human-centric dimension to trust evaluation.