Dynamic Federated Learning With Differential Privacy for Complex Vehicular Crowdsensing Systems Under Mobility Consideration
Xing Chang, Mohammad S. Obaidat, Chenbin Xu, Jingxiao Ma, Xiaoping Xue, Yantao Yu · IEEE Systems Journal · 2025
Vehicular crowdsensing (VCS) leverages data exchange among intelligent connected vehicles (ICVs) to support diverse intelligent applications, making privacy protection essential due to the sensitivity of shared data. Federated learning (FL) combined with differential privacy (DP) has emerged as a powerful approach for safeguarding privacy in such distributed systems. Due to the unique characteristics of vehicular networks, many VCS tasks are inherently linked to spatial and temporal factors. This article focuses on these VCS tasks within the context of a complex system, where ICV mobility serves as a central factor that dynamically influences interconnected aspects such as task matching accuracy, participation rates, and DP noise consistency. The multifaceted impact of ICV mobility on these elements requires a comprehensive analysis to understand its cascading effects on privacy protection and model performance within FL-based VCS. To address these complexities, we propose DFed-ADP, a dynamic FL framework with an adaptive DP mechanism tailored for VCS. DFed-ADP includes a rigorous theoretical derivation to quantify the influence of ICV mobility on the DP noise scale and a dynamic ICV selection strategy that prioritizes data importance and adapts to mobility patterns. Specifically, we introduce an adaptive user-level DP and derive a closed-form expression to quantify the impact of ICV mobility on noise variance. Then, based on theoretical analysis, we propose an efficient ICV selection scheme that ensures participating ICVs can provide high-value data and complete training tasks on time. These designs ensure a robust balance between privacy and model performance. Experimental results demonstrate the adaptability and efficiency of DFed-ADP, achieving significant accuracy improvements under independent and identically distributed (IID) and non-IID settings in VCS.