MoFed-Guard: a mobility-aware federated and explainable lightweight Transformer framework for misbehavior detection in VANETs

Authenticated vehicles can disseminate false safety messages that pass cryptographic verification but remain behaviorally untruthful, making insider misbehavior a challenge in Vehicular Ad Hoc Networks (VANETs). This study proposes MoFed-Guard , a data-local federated framework combining OBU-TFT , a compact temporal Transformer, with reliability-aware aggregation and on-demand SHAP explanation. Fixed RSUs act as training clients and OBUs perform inference. MoFed-Guard weights client updates using vehicle-association stability, communication reliability, and leave-one-client-out update consistency; these indicators are evaluated empirically rather than treated as theoretically derived trust variables. On the trajectory-disjoint 20-class VeReMi Extension test partition, the framework achieved 95.20% accuracy, 94.75% macro-precision, 94.30% macro-recall, and 94.52% macro-F1. With architecture and training fixed, MoFed-Guard improved macro-F1 by 1.45 points over FedAvg and 0.61 points over FedAdam; the five-seed improvement was significant [ t (4) = 6.30, p = 0.003, d z = 2.82]. Five-seed convergence reached 98.0 ± 0.2% validation macro-F1 at round 100, while held-out client-scale macro-F1 increased from 93.10% with three clients to 94.52% with ten. Explicit FL-layer delivery tests and a road-network-aware partition preserved the aggregation benefit, whereas coordinated-client corruption exposed the limitation of cosine-based consistency. The joint reliability score correlated positively with update utility (ρ = 0.71, p < 0.001). OBU-TFT contains 85,460 trainable parameters, occupies 0.34 MB, and achieved 5.10 ± 0.42 ms T4 inference latency. Parameter-matched comparison showed that OBU-TFT achieved higher macro-F1 than Linformer and Performer, although Linformer remained smaller and faster. These results support empirical reliability-conditioned federated weighting under simulation without implying universal convergence, Byzantine robustness, formal privacy, or field deployment readiness.