TY - GEN
T1 - On-Demand Incentive Design for Security-Defense Resource Allocation in 6G Vehicular Edge Learning
AU - Li, Hongyang
AU - Lin, Xi
AU - Wu, Jun
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported in part by the National Natural Science Foundation of China under Grant 61972255.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - On-demand
KW - budget-feasible
KW - incentive design
KW - resource allocation
KW - security defense
KW - vehicular edge learning
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U2 - 10.1109/ICC45855.2022.9838704
DO - 10.1109/ICC45855.2022.9838704
M3 - Conference contribution
AN - SCOPUS:85137273606
T3 - IEEE International Conference on Communications
SP - 1421
EP - 1426
BT - ICC 2022 - IEEE International Conference on Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Communications, ICC 2022
Y2 - 16 May 2022 through 20 May 2022
ER -