In the Social Internet of Vehicles (SIoV), federated learning is able to significantly protect the private data of the vehicle’s client, while reducing the transmission load between entities. Nevertheless, data can still be stolen by an adversary who analyzes the parameters uploaded by the client to steal it. In this paper, to effectively prevent data leakage and reduce the propagation delay of data, we design a federated learning collaborative authentication protocol for shared data. The parameters of the vehicle client model are encrypted by the protocol in the federated learning. The vehicle and other entities of the protocol realize efficient anonymous mutual authentication and key agreement. The security of the proposed protocol is proved in the stochastic predictive machine model. The simulation results on the SUMO and OMNeT++ platforms show that the authentication delay is the lowest compared to other protocols and the packet loss rate is reduced to 4.68%. Moreover, the overfitting of the globally aggregated model is effectively resolved.

User alignment can associate multiple social network accounts of the same user. It has important research implications. However, the same user has various behaviors and friends across different social networks. This will affect the accuracy of user alignment. In this paper, we aim to improve the accuracy of user alignment by reducing the semantic gap between the same user in different social networks. Therefore, we propose a semantically enhanced social network user alignment algorithm (SENUA). The algorithm performs user alignment based on user attributes, user-generated contents (UGCs), and user check-ins. The interference of local semantic noise can be reduced by mining the user’s semantic features for these three factors. In addition, we improve the algorithm’s adaptability to noise by multi-view graph-data augmentation. Too much similarity of non-aligned users can have a large negative impact on the user-alignment effect. Therefore, we optimize the embedding vectors based on multi-headed graph attention networks and multi-view contrastive learning. This can enhance the similar semantic features of the aligned users. Experimental results show that SENUA has an average improvement of 6.27% over the baseline method at hit-precision30. This shows that semantic enhancement can effectively improve user alignment.

Federated learning has been extensively utilized in distributed learning scenarios for the Internet of Vehicles. However, two key challenges exist: 1) Gradients are frequently transmitted between vehicles during FL training, which reduces the communication timeliness between the traffic participants. 2) The fixed gradient compression method cannot sufficiently adapt to the dynamic IoV network topology. Therefore, we design an adaptive vehicle clustering method constructed according to multiple attributes, such as computational resources, communication distance, and latency. Accordingly, we propose a dynamic gradient compression strategy that filters similar local training models between vehicles and uses the Wasserstein distance to compute a sparsity threshold. This threshold acts as a dynamic compression factor that compresses gradient model parameters, reducing redundant parameter transmission. Furthermore, we conduct experiments using two datasets to evaluate the proposed strategy’s effectiveness. The compression ratio improved by 132-and 178-fold compared to the baselines, and the aggregated accuracy increased by an average of 10.13%. Additionally, experiments incorporating communication noise revealed that the aggregation model of the signal noise ratio is -29 dB.

The capacity to diagnose faults in rolling bearings is of significant practical importance to ensure the normal operation of the equipment. Frequency-domain features can effectively enhance the identification of fault modes. ... | Find, read and cite all the research you need on Tech Science Press

The Industrial Internet of Things (IIoT) deepens the collaborative computing between different devices and control layers. In an IIoT, as a dynamic, time-varying, and complex environment, the digital twin (DT) drives the real-time dynamic mapping between physical devices and twins to achieve global control of industrial production lines. This article reviews the relevant research on the application and deployment of DT in the IIoT, which provides technical support for cross-production line data sharing and accurate decision-making. In particular, the related work of the multilayer collaborative computing architecture built by federated learning (FL) in DT-IIoT is reviewed. We systematically explore the key technical breakthroughs of DT and FL in the IIoT, such as data security collaboration, dynamic resource scheduling, computing efficiency improvement, and cross-domain collaborative computing, from four technologies: edge computing, blockchain, deep reinforcement learning (DRL), and personalized learning (PL). On this basis, we have explored and experimented in detail with fully decentralized SL in IIoT. SL-knowledge distillation (KD) can solve the challenge of reducing the reliability of global model decisions in SL due to multisource heterogeneous data in complex industrial scenarios. Experimental results show that swarm learning-based knowledge distillation (SrwamKD) outperforms other baselines by an average of 0.024 and 0.060 in recall and accuracy, respectively. In addition, we discuss the current challenges and future research directions.