With the rapid advancement of wearable devices and Internet of Things (IoT) technologies, sensor data generated by edge devices has surged. This data is crucial for advancing IoT applications, including health status monitoring, abnormal behavior detection, and environmental monitoring. However, traditional centralized learning requires uploading data to a central server, raising security and privacy concerns and hindering data application. Federated learning (FL) offers a solution by enabling collaborative model training on IoT devices without transferring data from the local device. In practice, edge devices generate data that is often highly heterogeneous, making it challenging for the global FL model to capture local data distributions accurately, leading to significant performance degradation. Additionally, imbalanced edge device resources and limited bandwidth can cause data transmission delays or interruptions, impacting application feasibility. To address these issues, we propose PFedKD, a novel personalized FL algorithm based on knowledge distillation, aimed at enhancing the model’s generalization ability and reducing communication overhead in heterogeneous IoT data environments. PFedKD constructs a public dataset using unlabeled pseudo data to extract knowledge from each client, training personalized models that fit local data distributions. This method controls dataset size while enhancing performance. During communication, only logits and class prototypes are transmitted, ensuring high communication efficiency. Sharpness aware minimization is introduced in local model training to optimize generalization. Additionally, we design a weight distribution mechanism based on client sample quality evaluation that optimizes knowledge aggregation and model personalization. Extensive experiments demonstrate that PFedKD significantly outperforms state-of-the-art baselines in both learning performance and communication efficiency.

Vertical federated learning (VFL) shows promise for enabling collaborative learning among Internet of Vehicle systems (IoVs) without requiring the sharing of private training data. However, existing work has exposed VFL’s vulnerability to privacy-stealing attacks, where an honest but curious server might reconstruct a client’s raw data from client-uploaded embeddings. In this work, we first elucidate the intrinsic mechanisms of privacy attacks from an information theory perspective, which provides a solid foundation for potential defensive strategies. Based on our findings, we introduce PriVFL, a defense mechanism based on information bottleneck theory. PriVFL is designed to safeguard the privacy of VFL-based IoVs by enabling shared embeddings to extract minimal information from input data, while preserving the information essential to target labels. Specifically, PriVFL restricts the information contained in embeddings by reducing the upper bound of mutual information between the raw samples and embeddings uploaded from local clients. Meanwhile, PriVFL ensures the effectiveness of the model by increasing the mutual information lower bound between embeddings and samples’ labels. Our evaluation includes 5 benchmark data sets and 4 different models. Experimental results demonstrate that PriVFL effectively mitigates privacy attacks while preserving the model’s effectiveness. These findings underscore that PriVFL can significantly enhance the privacy of VFL-based IoVs, thereby bolstering the development of practical IoV applications.

Vertical Federated Learning (VFL) is susceptible to various one-party hijacking attacks, such as Replay and Generation attacks, where a single malicious client can manipulate the model to produce attacker-specified results, thereby compromising its reliability in real-world deployments. In this paper, we first uncover the underlying mechanisms of these attacks and observe that successful attacks induce significant discrepancies in the embedding-label associations across different clients. We establish a theoretical framework demonstrating how these discrepancies can serve as reliable indicators for detecting hijacking attempts. Building upon this insight, we propose VFLMonitor, a robust defense mechanism that leverages these embedding-label discrepancies to detect and mitigate hijacking attacks. Specifically, VFLMonitor identifies suspicious queries by analyzing differences in label estimations from multiple clients and applies a majority voting rule to correct or filter out these malicious queries. Moreover, VFLMonitor introduces a novel regularization strategy during training to reduce intra-class variance in embeddings, thereby enhancing their discriminative power and improving defense effectiveness. Extensive experi21 ments were conducted on 5 real-world datasets against 2 different attack types under 3 attack scenarios. The results demonstrate that VFLMonitor can effectively identify and exclude potential hijacked requests in all types of one-party hijacking attacks, while maintaining a meager false positive rate for legitimate queries.

Federated recommendation (FR) is a decentralised approach to training personalised recommender systems, protecting users' privacy by avoiding data collection. Despite its privacy advantages, FR remains vulnerable to poisoning attacks. We focus on untargeted poisoning attacks against FR which degrade the overall performance of recommender services, leading to a detrimental impact on user experience and service quality. In this paper, we propose a general framework to formalise untargeted attacks and identify the vital role played by the interplay between items and user profiles in determining FR's performance. We present an untargeted attack FRecAttack2 which exploits this interplay. Specifically, we develop various methods for sampling user profiles, which approximate user distributions with and without collusion among malicious users. Then we leverage a new measurement to identify items that can disrupt the original interplay with user profiles, based on the change velocity of items' recommendation scores during optimisation. Extensive experiments demonstrate the superiority of our attack, outperforming existing methods by up to 27.56%, and its stealthiness in evading mainstream defences. To counteract untargeted attacks, we present a defence GuardCQ to detect malicious users by quantifying their contribution to boost the right interplay between items and user profiles. Empirical results show that GuardCQ effectively mitigates the attack's impact on FR and enhances the robustness of FR against poisoning attacks.

Graph neural networks, effectively harnessing the extensive interactive data from Internet of Things (IoT) devices, significantly enhance service quality in IoT systems. However, traditional centralized training leads to the leakage of private data during the data collection and model training phases in IoT scenarios. Federated learning (FL) has emerged as a promising approach, facilitating collaborative model training across diverse IoT devices without sharing sensitive data. The intricate types and relationships among IoT devices from various institutions highlight the issues of class imbalance and graph heterogeneity across different clients. These issues decrease the performance of FL models. In this work, we focus on a more realistic scenario where the IoT institutions have only limited amount and types of data. We propose a cross-institutional federated heterogeneous graph learning method called FedHGL. It aims to mitigate the negative effects of class imbalance while maintaining the private data locally on clients during collaborative training. We employ a heterogeneous graph neural network as the training model for clients. FedHGL generates cross-client minority class samples to enhance the model performance. Additionally, it incorporates a compensation mechanism to prevent forgetting global information. FedHGL designs an adaptive aggregation coefficient that assigns weights to IoT institutions according to the class imbalance of their data, thereby optimizing the aggregation process. Extensive experiments demonstrate the effectiveness of FedHGL for class imbalance and heterogeneous graph data.

Aspect-based sentiment analIsis (ABSA) aims to detect the sentiment polaritI of a specific aspect in an opinionated sentence. Current work focuses on exploiting the sIntactic tree to shorten the distance between the aspect term and context words. However, the “hard-pruning” strategI on the sIntactic tree maI lead to the reduction of importa nt sIntactic information. In this paper, we propose a novel sInt actical distance attention guided graph convolutional network (SDGCN) for ABSA. Our model is capable of fullI exploiting the sIntactic knowledge with a “soft pruning” strategI and learning crucial fine-grain sIntactic distance info rmation. AdditionallI, an effective denselI connected graph convolutional laIer is applied to avoid the over-sm oothing problem of standard GCN. Experiments conducted on three benchmark datasets show that our model achieves promising results comparing to the baseline models.

The thermal characteristics of bearing rotating elements are essential for the service evaluation of bearing. However, the existing monitoring methods are limited by bearing internal complex structure and extreme operating environment. Hence, based on the monitoring technology of CdTe quantum dots (QDs) sensor, an improved QDs sensor was fabricated by process optimization and polymers coating, which enables bearing rotating elements thermal monitoring for extreme working condition. First, the principle of the measurement was introduced and the existing problems were analyzed. Then, the influence of preparation process on temperature-dependent photoluminescence (PL) spectra of CdTe QDs was investigated. The performance improvement methods of the QDs sensor, both organic coating and inorganic coating, were investigated. The fabricated sensor named CdTe@PVA presents remarkable improved performance in fluorescence intensity, highest tolerable temperature and stability. In the end, a rolling bearing experimental rig was set up and the temperature of the bearing cage at different rotation speeds were captured by using the proposed CdTe@PVA sensor.

To efficiently mine the classification model for machine fault diagnosis based on images, a hybrid classification algorithm, which inspired by combining nonnegative matrix factorization and artificial immune system, was put forward. In the algorithm, nonnegative matrix factorization was employed for dimensionality reduction of the time-frequency spectral images. An artificial immune based classification model was constructed by means of training of data samples mapped into low-dimensional space to recognize the machine conditions and diagnose faults. Experimental results on the fault classification of diesel valve train demonstrate the effectiveness of the algorithm. Compared with probabilistic neural network classifiers, the hybrid classifier achieves better fault diagnosis performance.