There has been a growing trend toward leveraging machine learning (ML) and deep learning (DL) techniques to optimize and enhance the performance of wireless communication systems. However, limited attention has been given to the vulnerabilities of these techniques, particularly in the presence of adversarial attacks. This paper investigates the adversarial attack and defense in distributed multiple reconfigurable intelligent surfaces (RISs)-aided multiple-input multiple-output (MIMO) communication systems-based autoencoder in finite scattering environments. We present the channel propagation model for distributed multiple RIS, including statistical information driven in closed form for the aggregated channel. The symbol error rate (SER) is selected to evaluate the collaborative dynamics between the distributed RISs and MIMO communication in depth. The relationship between the number of RISs and the SER of the proposed system based on an autoencoder, as well as the impact of adversarial attacks on the system’s SER, is analyzed in detail. We also propose a defense mechanism based on adversarial training against the considered attacks to enhance the model’s robustness. Numerical results indicate that increasing the number of RISs effectively reduces the system’s SER but leads to the adversarial attack-based algorithm becoming more destructive in the white-box attack scenario. The proposed defense method demonstrates strong effectiveness by significantly mitigating the attack’s impact. It also substantially reduces the system’s SER in the absence of an attack compared to the original model. Moreover, we extend the phenomenon to include decoder mobility, demonstrating that the proposed method maintains robustness under Doppler-induced channel variations.

In vehicular networks (VNets), vehicular federated learning (VFL) is a new learning paradigm that can protect data privacy of vehicle nodes (VNs) while training models. In VFL, the importance of data (IoD) is a key factor that affects model training accuracy. However, due to the heterogeneity of data in the VFL, it is a challenge to evaluate the quality of data owned by different VNs and design an efficient federated learning scheme to enable the VNs to complete learning tasks collaboratively. In this paper, we consider the IoD and propose a redundancy-aware collaborative federated learning (RCFL) scheme for the VFL. In the scheme, by jointly considering the data quality and the cooperation among VNs, we first design a redundancy-aware federated learning architecture to efficiently provide learning services in VNets. Then, we develop a data importance model that integrates the non-independent and identically distributed (non-IID) degree and the redundancy of data (RoD) to evaluate the data quality and formulate the cooperation of the VNs as a coalition game to improve their data importance, where the equilibrium of the coalition game is obtained by designing a coalition formation algorithm. After that, by considering the diversified characteristics of data and the available resources of different VNs in each coalition, a coalition-based federated learning algorithm is designed to enable the distributed coalitions to complete the learning task cooperatively with the target of improving the learning accuracy. The simulation results show that the proposed scheme outperforms the benchmark schemes in terms of the IoD obtained by the VNs and the training accuracy.

The autonomous vehicles (AVs), as intelligent mobile robots, can undertake tasks to facilitate various computation-intensive services in intelligent transportation system (ITS). Due to hardware device failures or environmental identification errors, the AVs controlled by intelligent algorithms may cause accidents during driving. However, the existing studies in the post-accident stage lack the analysis of the impact degree of the accidents and the computing tasks undertaken by the AVs to determine the optimal maintenance strategy. In this article, we consider the accidents in a continuous period of time and design a digital twin (DT)-enabled post-accident maintenance scheme. Specifically, by considering the computing tasks undertaken by the AVs and the impact degree of the accidents, we first design a DT-enabled post-accident maintenance architecture. With the designed architecture, an optimal maintenance method under an incomplete information scenario is then proposed to help each accident AV decide its optimal maintenance strategy. Besides, based on the maintenance strategies of the AVs and the capacities of the maintenance service providers (MSPs), the two-way selection problem between the AVs and the MSPs in the continuous period of time is modeled as a dynamic matching game to obtain the optimal AV-MSP pairs. Simulation results demonstrate that the proposed scheme outperforms the benchmark schemes in terms of the maintenance rate of the accident AVs, the average utility of the MSPs, and the average social welfare.

In the heterogeneous vehicular networks (HetVNets), the base stations (BSs) can exploit the massive amounts of valuable data collected by vehicles to complete federated learning tasks. However, most of the existing studies consider the scenario of one task requester (TR) and ignore the fact that multiple TRs may concurrently generate their model training requests in the HetVNets. In this paper, we consider the scenario of multi-TR and multi-BS and propose a digital twin enabled scheme for multitask federated learning to address the two-way selection problem between the TRs and the BSs. We first analyze the diversified requirements of the TRs in the HetVNets. Then, we develop a novel model that jointly considers the available training data, the declared price, and the training experience to evaluate the differentiated training capabilities of the BSs. After that, based on the requirements of the TRs and the training capabilities of the BSs, the two-way selection problem between the TRs and the BSs is formulated as a matching game in the digital twin networks, where a matching algorithm is designed to obtain their optimal strategies. The simulation results demonstrate that the proposed scheme can obtain the highest model accuracy and bring the highest utility to the TRs compared with the conventional schemes.