BSFL: Secure and Efficient Blockchain-Based Split Federated Learning for Internet of Vehicles

The rapid development of the automotive industry and the Internet of Vehicles (IoV) has led to an exponential growth of distributed vehicular data, driving the need for secure and efficient collaborative machine learning solutions. However, existing distributed collaborative machine learning (DCML) approaches, such as federated learning and split learning, face significant challenges in IoV scenarios, including limited training efficiency, centralized aggregation vulnerabilities, and constrained privacy and model protection. To address these issues, we propose a blockchain-based split federated learning (BSFL) scheme for IoV applications. BSFL non-trivially combines federated learning and split learning to enable vehicles with low computational power to participate in parallel training, improving both model accuracy and training efficiency. By utilizing blockchain as a decentralized infrastructure, BSFL eliminates the risks of single points of failure and ensures model consistency through Byzantine fault-tolerant consensus. Furthermore, we design a noise addition mechanism based on differential privacy to safeguard client data privacy and model security. Formal security analysis and extensive experiments demonstrate that BSFL achieves enhanced privacy, security, and training performance. Comparing to related DCML schemes, BSFL reduces computational overhead by up to 88.84% and client training time by up to 29.49% while maintaining comparable accuracy. When training on ResNet-50 based on CIFAR10, BSFL achieved an accuracy of 93.15%. And the verification process for each model’s training results on the blockchain requires 1.49 ms.