On-Demand Incentive Design for Security-Defense Resource Allocation in 6G Vehicular Edge Learning

In the 6G era, the Intelligent Internet of Vehicles (IIoV) usually faces multiple security threats, which causes a trade-off of computation resources between security defense and vehicular artificial intelligence (AI). On-demand resource allocation in accordance with attack strength is a must for 6G vehicular edge learning. The existing works just focus on realizing low latency for AI resource allocation in vehicular edge learning, which ignores vehicles' high security-defense demands for computation resources in the face of attacks. To address this, this paper proposes an on-demand incentive mechanism to achieve coordinated optimization of security defense resource allocation over 6G vehicular edge learning. First, we propose budget-feasible incentive contracts for computation resource allocation based on vehicles' security-defense demands, which maximizes the learning utility of each vehicle type with a particular demand level. The contracts are tailored with the optimal resource allocation and incentive rewards with respect to different demand sensitivities. Next, apart from minimizing the single iteration time with the designed contracts, we design an optimization model of learning parameters for local accuracy to minimize the overall iteration time. Finally, simulation results show the feasibility and efficiency of the security-defense recourse allocation. This work is significant to improve the defense capability of 6G vehicle-edge learning against dynamic threats.