The development of the Internet of Things (IoT) has led to the widespread use of WiFi-enabled consumer electronic devices, which are now common in everyday life. These advancements in IoT have greatly improved data collection and analysis capabilities, especially for health monitoring applications. However, traditional centralized machine learning methods often fall short, raising significant privacy concerns and requiring extensive data collection, which is inefficient. To address these limitations within the distributed IoT environment, this article presents a federated learning (FL)-based WiFi sensing system specifically designed for health monitoring. By enabling local model training, our system prevents the sharing of sensitive data, thus reducing the risk of privacy breaches. We further enhance our system with a secret sharing mechanism coupled with model sparsification to significantly improve privacy. Additionally, our improved top-k model sparsification algorithm, equipped with adaptive residuals, reduces communication overhead while ensuring high accuracy. Extensive testing across various datasets and models confirms that our system outperforms existing benchmarks in terms of privacy protection and communication efficiency, marking a substantial advancement in health monitoring within the IoT.

The Internet of Things (IoT) connects physical devices to the Internet via open communication protocols. Malicious actors can exploit vulnerabilities to steal data or manipulate critical IoT settings, so there is a need for strong security measures. We propose an improved real-time intrusion detection system (IDS) called the real-time credibility system (RTCS), which utilizes traffic statistics and authentication analysis to compute credibility. RTCS performs the authentication process by utilizing elliptic curve encryption and decryption operations, basic symmetric encryption, and hash functions. This process enables anonymous mutual authentication between IoT devices. Subsequently, RTCS accesses sparsified user history data and introduces flexibility in calculating user credibility by employing an adapted secondary paradigm combined with preset “tolerance parameters,” which serve as optimal thresholds for classifying different users. When a normal user violates regulations, their credibility decreases by a specified degree. If a high-risk user commits another violation, RTCS cannot tolerate it, leading to a rapid decline in their credibility. RTCS implements diversion measures and provides assisted decision scores for different users. Experimental results demonstrate that our method achieves an F1-score of 0.9707 and an area under the curve score of 0.9535. Compared to other works, RTCS exhibits superior performance and proactivity.

Traffic sign recognition (TSR) is vital for vehicle safety and navigation, especially in the era of autonomous cars. Internet of Vehicles (IoV) provide a promising infrastructure for vehicular networks due to their agility and interoperability. However, privacy concerns and network restrictions hinder the collection of massive data from distributed automotive sensors in IoV. To address these challenges, this article proposes the application of federated learning (FL) and model sparsification to optimize traffic sign recognition (TSR) in autonomous vehicles. FL enables decentralized learning while preserving data privacy, and model sparsification significantly reduces communication costs. Furthermore, we incorporate the Adam optimizer for local training, ensuring efficient model optimization on each vehicle. Experimental results demonstrate the effectiveness of our approach, with improved TSR performance while mitigating privacy risks and enhancing communication efficiency. This research contributes to the advancement of TSR in IoV by introducing FL, model sparsification, and the use of the Adam optimizer for local training, facilitating efficient and privacy-preserving vehicular network learning.

The Internet of Things (IoT) has evolved into a global platform dramatically facilitating human life through intelligent services. It is straightforward for people to access smart devices through IoT. However, the easy accessibility of IoT devices has also led to unprecedented security challenges for the IoT. To ensure the security of the basic structure of IoT, we need to establish a security barrier that can filter malicious access to IoT devices and achieve the integration of intrusion detection systems (IDSs) with intelligent gateways. This article establishes threat models of Denial of Service, Replay, man-in-the-middle, and Loophole attacks based on statistical flow characteristics and identity authentication. It uses supervised learning to obtain the credibility index to protect the IoT system. We use the Django framework to verify identity authorization information, the decision tree to determine request attributes, and the real-time status feedback from IoT devices to perform a risk assessment on the current user by precalculating the importance ratio (Ir), the maximum credibility index$(P_{\mathrm {max}})$, and the minimum credibility index$(P_{\mathrm {min}})$. With administrator verification, we conduct a convergence analysis to obtain user attributes. The experimental results show that our approach achieves a recognition accuracy of 94.7%.

Physical layer channel key generation is a promising technology for secure communication in wireless network security , which mainly establishes secure communication keys between any two legitimate users. This article reviews current techniques for physical layer channel key generation. The physical layer channel key generation principle, system model, attack model, key generation process, and experimental application are comprehensively reviewed. In addition, we introduce the experimental scenarios of key generation and the collection methods of channel measurements in wireless applications and simulation platforms and summarize the experimental equipment configuration and actual operation in the process of collecting different measurements. The article concludes with some suggestions for future research.

The development and popularity of social networks have made information dissemination unprecedentedly convenient and speedy. However, the spread of fake news can often cause serious harm to society and individuals. Therefore, machine learning-based fake news detection methods have become increasingly important. The existing work often needs to collect sufficient user-side data for training, which also boosts the privacy leakage risk to the users. Therefore, this article proposes an intelligent fake news detection system based on federated learning (FL) called FIND, which can train a global model while keeping user data locally. At the same time, we also designed a sparsified update perturbation method to enhance the system security further. Finally, we conduct simulation experiments to study and discuss multiple acoustic factors and prove the feasibility of our system in terms of accuracy, security, and efficiency.

The concept of the metaverse, first introduced in science fiction, is rapidly becoming a technological reality with profound implications for various sectors, including healthcare. By merging virtual reality (VR), augmented reality (AR), artificial intelligence (AI), and advanced communication technologies, the metaverse promises to create immersive, interactive environments that can transform medical practice, education, and patient care [1].

The Internet of Medical Things (IoMT) has a bright future with the development of smart mobile devices. Information technology is also leading changes in the healthcare industry. IoMT devices can detect patient signs and provide treatment guidance and even instant diagnoses through technologies, such as artificial intelligence (AI) and wireless communication. However, conventional centralized machine learning approaches are often difficult to apply within IoMT devices because of the difficulty of large-scale collection of patient data and the potential risk of privacy breaches. Therefore, we propose a blockchain-based two-stage federated learning approach that allows IoMT devices to train a global model collaboratively without gathering the data to a central server. Specifically, to address the problem of poor training performance on non-independent identically distributed (non-IID) data, we design a blockchain-based data-sharing scheme that can significantly improve the model’s accuracy without threatening user privacy. We also design a client selection mechanism to further improve the system’s efficiency. Finally, we validate the feasibility and effectiveness of our system through simulation experiments on three popular datasets (i.e., MNIST, Fashion-MNIST, and CIFAR-10).

The rapid growth of artificial intelligence (AI), blockchain technology, and edge computing services have enabled the Internet of Medical Things (IoMT) to provide various healthcare services to patients, including neural network-based disease diagnosis, heart rate monitoring, and fall detection. Generally, end devices should transmit the collected patient data to a centralized server for further model training, but at the same time, the patient's privacy may be at risk. In addition, due to the diversity of patient conditions, a one-size-fits-all model cannot meet personalized healthcare needs. To address the above challenges, we propose a blockchain-based personalized federated learning (FL) system that enables clients to participate in personalized model training without directly uploading private data. We further realize the decentralized FL by combining blockchain technology, which improves the security level of the system. Finally, we verify the reliable performance of our system on different datasets through simulation experiments.

With the improvement of computing power and the development of network technology, Internet of Things (IoT) devices are widely used in many industries. But it also faces various security threats. Anomaly detection is a commonly used method, but traditional methods face shortcomings such as low accuracy. Therefore, in this paper, we introduce a decentralized federated learning method for anomaly detection, using neural networks to improve accuracy and take advantage of the characteristics of federated learning to protect local data security. The decentralized algorithm avoids the drawbacks of traditional federated learning such as the single point of failure. Finally, we conduct simulation experiments on the IoT23 dataset, which verify the performance of our system.

For data isolated islands and privacy issues, federated learning has been extensively invoking much interest since it allows clients to collaborate on training a global model using their local data without sharing any with a third party. However, the existing federated learning frameworks always need sophisticated condition configurations (e.g., sophisticated driver configuration of standalone graphics card like NVIDIA, compile environment) that bring much inconvenience for large-scale development and deployment. To facilitate the deployment of federated learning and the implementation of related applications, we innovatively propose WebFed, a novel browser-based federated learning framework that takes advantage of the browser’s features (e.g., Cross-platform, JavaScript Programming Features) and enhances the privacy protection by applying local differential privacy. Finally, We conduct experiments on heterogeneous devices to evaluate the performance of the proposed WebFed framework.

Abstract Federated generative adversarial networks are designed to collaborate across the communication and privacy-constrained edge servers participating in training. However, in the Internet of Things scenario, local updates uploaded by edge servers can lead to the risk of privacy breaches. Gradient-sanitized-based approaches can transmit sanitized sensitive data with strict privacy guarantees, but gradient clipping and perturbation severely degrade convergence performance. In this paper, our proposed algorithm enhances the privacy of terminated raw data through differential privacy before it is transmitted to the edge server. The edge server trains the local generator and discriminator using the perturbed data, which provides privacy guarantees for the gradient attack on the FedGAN without compromising the gradient accuracy. The results of the experimental evaluation show that the algorithm generates images with slightly better quality than that generated by the gradient-sanitized-based approaches while maintaining privacy.

Federated learning can collaboratively train a global model without gathering clients’ private data. Many works focus on reducing communication cost by designing kinds of client selection method or averaging algorithm. But they all consider whether the client will participant or not, and the training time could not be reduced as data size of update for each client is not changed. We proposed COFEL, a novel federated learning system which can both reduce the communication time by layer-based parameter selection and enhance the privacy protection by applying local differential privacy mechanism on the selected parameters. We present COFEL-AVG algorithm for global aggregation and designed layer-based parameter selection method which can select the valuable parameters for global aggregation to optimize the communication and training process. And it can reduce the update data size as only selected part will be transferred. We compared with traditional federated learning system and CMFL which also applies a parameter selection method but model-based and performed experiments on MNIST, Fashion-MNIST and CIFAR-10 to verify the effectiveness of COFEL. The results denoted that it can improve at most 22.8% accuracy compared with CMFL on CIFAR-10 and reduce around 20% and 48% training time to reach an accuracy of 0.85 compared with traditional FL and CMFL on Fashion-MNIST dataset.