Frozen Watermark-Based Federated Learning with Incentive Mechanism for Electric Vehicle Suspensions in Vehicular Communication Systems

Federated learning for electric vehicle suspension (FLEVS) is a key technology to address the limitations of battery capacity and the rising use of sensing cameras in electric vehicles. However, current FLEVS systems face challenges such as energy constraints, privacy risks, and copyright issues in car enterprise cloud (CEC) trained models. This paper proposes a frozen watermark-based federated learning incentive mechanism to tackle these issues. It incorporates a global frozen watermark to leverage continuous learning while minimizing training interference. Additionally, a Stackelberg game-based incentive mechanism between the CEC and mobile vehicles (MVs) optimizes information security strategies. The derived optimal reward function and iteration parameters are validated through numerical results, demonstrating the scheme's effectiveness.