CSI-FL: Communication-Sensing Integrated Federated Learning Framework for Heterogeneous IoV

Communication and sensing integration (CSI) technology underpins efficient data acquisition, real-time sensing, and intelligent decision-making in intelligent transportation systems (ITS). By merging communication and sensing, CSI enables seamless data sharing and collaborative learning within the internet of vehicles (IoV), while tackling the complexities of dynamic, heterogeneous environments. However, IoV systems still confront suboptimal resource allocation, synchronization bottlenecks in federated learning (FL), and the delicate balance between privacy and data utility, limiting scalability and deployment. To address these issues, this article presents a CSI-driven federated learning framework comprising three key modules: the polar-driven resource allocation mechanism (PDRAM), the polar-driven asynchronous federated update mechanism (AFUM), and the deep context-aware dynamic privacy budget allocation model (DC-DPBA). Leveraging polar coding principles, PDRAM optimizes communication channel allocation by prioritizing high-fidelity data on high-speed channels. AFUM adopts a vehicle performance score and a progressive aggregation strategy for asynchronous updates, mitigating synchronization challenges. Meanwhile, DC-DPBA uses deep learning and contextual information to dynamically adjust privacy budgets, striking a balance between data utility and privacy protection. Experimental results show that this framework increases transmission efficiency, model training accuracy, and privacy preservation by 22%, 17%, and 17%, respectively, compared to state-of-the-art approaches, offering a scalable and secure solution for CSI-driven IoV environments.