CKDP-PINNFed: Dual-Layer Privacy Framework Combining Contextual K-Anonymity and Differential Privacy with Physics-Informed Federated Learning for Explainable Crowdsourced Road Anomaly Detection

Crowdsourced smartphone-based road condition monitoring offers a cost-effective alternative to traditional infrastructure surveys; however, widespread adoption is hindered by serious privacy concerns. Raw sensor data reveals precise locations, speed violations, and behavioral patterns, deterring user participation due to risks of surveillance, re-identification, and misuse by authorities and insurers. Existing federated learning approaches protect only model gradients while leaving raw data exposed locally, and standalone privacy methods such as k-anonymity and differential privacy require centralized aggregation, introducing single points of failure and failing to balance privacy with utility for effective road anomaly detection. We propose CKDP-PINNFed, dual-layer privacy-preserving federated framework that addresses these limitations. First, we apply local contextual k-anonymity combined with differential privacy to anonymize raw sensor data before storage, achieving a 78.4% reduction in composite privacy loss score and a 99.1% reduction in uniqueness risk compared to differential privacy alone. Second, we employ FedProx with a hybrid Physics-Informed Neural Network and Random Forest (PINN-RF) architecture for decentralized training on anonymized data. The lightweight framework (0.19M parameters, 0.73 MB) achieves real-time processing at 0.0322 ms total latency per sample, comprising 0.0199 ms for CK-DP preprocessing and 0.0123 ms for PINN-RF inference, enabling practical on-device deployment. Experimental results on the RoadSens-4M dataset demonstrate an F1-score of 89.94% ± 0.35% and accuracy of 89.93% ± 0.19%, outperforming FedAvg by 1.64% and all other federated baselines under extreme non-IID conditions. Ablation studies confirm component synergy, SHAP analysis verifies physically meaningful feature interpretability, and external validation achieves 84.23% ± 1.18% accuracy on an independent dataset. These results establish CKDP-PINNFed as a practical and secure solution for trustworthy crowdsourced road monitoring that protects individual privacy.