The rapid evolution of artificial intelligence(AI)technologies has significantly propelled the advancement of the Internet of Vehicles(IoV).With AI support,represented by machine learning technology,vehicles gain the ...The rapid evolution of artificial intelligence(AI)technologies has significantly propelled the advancement of the Internet of Vehicles(IoV).With AI support,represented by machine learning technology,vehicles gain the capability to make intelligent decisions.As a distributed learning paradigm,federated learning(FL)has emerged as a preferred solution in IoV.Compared to traditional centralized machine learning,FL reduces communication overhead and improves privacy protection.Despite these benefits,FL still faces some security and privacy concerns,such as poisoning attacks and inference attacks,prompting exploration into blockchain integration to enhance its security posture.This paper introduces a novel blockchain-enabled federated learning(BCFL)scheme with differential privacy(DP)tailored for IoV.In order to meet the performance demanding IoV environment,the proposed methodology integrates a consortium blockchain with Practical Byzantine Fault Tolerance(PBFT)consensus,which offers superior efficiency over the conventional public blockchains.In addition,the proposed approach utilizes the Differentially Private Stochastic Gradient Descent(DP-SGD)algorithm in the local training process of FL for enhanced privacy protection.Experiment results indicate that the integration of blockchain elevates the security level of FL in that the proposed approach effectively safeguards FL against poisoning attacks.On the other hand,the additional overhead associated with blockchain integration is also limited to a moderate level to meet the efficiency criteria of IoV.Furthermore,by incorporating DP,the proposed approach is shown to have the(ε-δ)privacy guarantee while maintaining an acceptable level of model accuracy.This enhancement effectively mitigates the threat of inference attacks on private information.展开更多
In this paper,the ^(90)Sr/^(90)Y coating effects on scattering width(SW) of cylindrical conductor targets are investigated.The electron density distribution of plasma around cylindrical targets of different radiuses i...In this paper,the ^(90)Sr/^(90)Y coating effects on scattering width(SW) of cylindrical conductor targets are investigated.The electron density distribution of plasma around cylindrical targets of different radiuses is simulated under different radioactivities in normal or oblique incidence.In normal incidence,the SWs are examined as functions of frequency and scattering angle;while in oblique incidence,the SW is inspected as a function of incident angle at the frequency of 1.5 GHz.The results obtained are compared with those from an ideal perfect electric conductor(PEC) cylinder.It is demonstrated that the SW decreases over a wide frequency range in the back scattering region by coating a ^(90)Sr/^(90)Y layer on the cylindrical target.Moreover,the reduction in bi-static SW amplitude can reach 3-20 dB,when the incident angle is smaller than 30° at 1.5 GHz.It is a significant improvement in the stealth effect.展开更多
基金supported in part by the Natural Science Foundation of Henan Province(Grant No.202300410510)the Consulting Research Project of Chinese Academy of Engineering(Grant No.2020YNZH7)+3 种基金the Key Scientific Research Project of Colleges and Universities in Henan Province(Grant Nos.23A520043 and 23B520010)the International Science and Technology Cooperation Project of Henan Province(Grant No.232102521004)the National Key Research and Development Program of China(Grant No.2020YFB1005404)the Henan Provincial Science and Technology Research Project(Grant No.212102210100).
文摘The rapid evolution of artificial intelligence(AI)technologies has significantly propelled the advancement of the Internet of Vehicles(IoV).With AI support,represented by machine learning technology,vehicles gain the capability to make intelligent decisions.As a distributed learning paradigm,federated learning(FL)has emerged as a preferred solution in IoV.Compared to traditional centralized machine learning,FL reduces communication overhead and improves privacy protection.Despite these benefits,FL still faces some security and privacy concerns,such as poisoning attacks and inference attacks,prompting exploration into blockchain integration to enhance its security posture.This paper introduces a novel blockchain-enabled federated learning(BCFL)scheme with differential privacy(DP)tailored for IoV.In order to meet the performance demanding IoV environment,the proposed methodology integrates a consortium blockchain with Practical Byzantine Fault Tolerance(PBFT)consensus,which offers superior efficiency over the conventional public blockchains.In addition,the proposed approach utilizes the Differentially Private Stochastic Gradient Descent(DP-SGD)algorithm in the local training process of FL for enhanced privacy protection.Experiment results indicate that the integration of blockchain elevates the security level of FL in that the proposed approach effectively safeguards FL against poisoning attacks.On the other hand,the additional overhead associated with blockchain integration is also limited to a moderate level to meet the efficiency criteria of IoV.Furthermore,by incorporating DP,the proposed approach is shown to have the(ε-δ)privacy guarantee while maintaining an acceptable level of model accuracy.This enhancement effectively mitigates the threat of inference attacks on private information.
基金supported by the 863 Program through the Ministry of Science and Technology(No.2006AA03Z458)the National Natural Science Foundation of China(Nos.10904061 and 50977042)
文摘In this paper,the ^(90)Sr/^(90)Y coating effects on scattering width(SW) of cylindrical conductor targets are investigated.The electron density distribution of plasma around cylindrical targets of different radiuses is simulated under different radioactivities in normal or oblique incidence.In normal incidence,the SWs are examined as functions of frequency and scattering angle;while in oblique incidence,the SW is inspected as a function of incident angle at the frequency of 1.5 GHz.The results obtained are compared with those from an ideal perfect electric conductor(PEC) cylinder.It is demonstrated that the SW decreases over a wide frequency range in the back scattering region by coating a ^(90)Sr/^(90)Y layer on the cylindrical target.Moreover,the reduction in bi-static SW amplitude can reach 3-20 dB,when the incident angle is smaller than 30° at 1.5 GHz.It is a significant improvement in the stealth effect.
