摘要
To provide diversified services in the intelligent transportation systems,smart vehicles will generate unprecedented amounts of data every day.Due to data security and user privacy issues,Federated Learning(FL)is considered a potential solution to ensure privacy-preserving in data sharing.However,there are still many challenges to applying the traditional synchronous FL directly in the Internet of Vehicles(Io V),such as unreliable communications and malicious attacks.In this paper,we propose a Directed Acyclic Graph(DAG)based Swarm Learning(DSL),which integrates edge computing,FL,and blockchain technologies to provide secure data sharing and model training in Io Vs.To deal with the high mobility of vehicles,the dynamic vehicle association algorithm is introduced,which could optimize the connections between vehicles and road side units to improve the training efficiency.Moreover,to enhance the anti-attack property of the DSL algorithm,a malicious attack detection method is adopted,which could recognize malicious vehicles by the site confirmation rate.Furthermore,an accuracy-based reward mechanism is developed to promote vehicles to participate in the model training with honest behaviors.Finally,simulation results demonstrate that the proposed DSL algorithm could achieve better performance in terms of model accuracy,convergence rates and security compared with existing algorithms.
基金
supported in part by the National Natural Science Foundation of China(NSFC)under Grant 62371082,61831002,and 62001076
in part by the Natural Science Foundation of Chongqing under Grant CSTB2023NSCQ-MSX0726 and cstc2020jcyj-msxmX0878。
