A Framework of Decentralized Federated Learning With Soft Clustering and 1-Bit Compressed Sensing for Vehicular Networks

Federated Learning (FL) has been recognized as a transformative approach in vehicular networks, enabling collaborative training between vehicles and preserving data privacy. However, the high mobility and dynamic topology changes inherent in vehicular environments pose significant challenges, primarily due to the increased communication overhead associated with exchanging model parameters. To mitigate these issues, a novel framework for decentralized wireless FL with soft clustering and 1-bit compressed sensing (SC1BCS-WFL) is proposed in this article. The framework considers vehicle position, vehicle attributes, vehicle speed, and model cosine similarity when grouping vehicles. It utilizes an adaptive threshold mechanism based on 1-bit compression to reduce uplink transmission load while maintaining FL performance. In addition, an early stopping strategy is incorporated into the proposed framework to avoid unnecessary waste of computational and communication resources. Simulation results show that the SC1BCS-WFL framework can enhance the efficiency of federated learning in vehicular settings, particularly with non-independent and identically distributed data. Simulation results also validate the framework's ability to reduce communication overhead while achieving high model accuracy, indicating its suitability for distributed Internet of Vehicles (IoV) scenarios and contributing to the development of smarter and more efficient IoV applications.