With the rapid proliferation of mobile sensing in fields such as personal health monitoring in data processing are becoming more prominent. This paper introduces a decentralized DETR framework inspired by blockchain proof-of-work consensus. The framework trains models locally on each device and evaluates the device's reputation based on its historical performance. Only devices meeting predefined criteria are admitted to the update committee, which enhances security. This mechanism reduces reliance on centralized servers and minimizes infrastructure costs. While a supervisory operator ensures the smooth operation of the system. To further enhance trust, we propose a credibility assessment method that integrates risk metrics with data quality scores via a non-cooperative game-theoretic model. By achieving Nash equilibrium, this method not only guarantees local optimality but also prioritizes users who provide high-quality, low-risk data, thereby promoting timely committee updates to achieve global optimality. As a complement to DETR, we propose BAL-IDS, an advanced intrusion detection system (IDS) that extracts latent features using autoencoders and dynamically fine-tunes the hyperparameters of OCSVM using a Bayesian joint local agent optimization strategy. This dual approach enhances the system's resilience to complex threats, especially those that exploit requester feedback mechanisms. Experiments show that our research is superior to traditional schemes. © 2025 Elsevier B.V.

Federated Learning (FL) enables privacy-preserving model training across distributed clients. However, its reliance on voluntary client participation makes it vulnerable to strategic behaviors—actions that are not overtly malicious but significantly impair model convergence and fairness. Existing defense methods primarily focus on explicit attacks, overlooking the challenges posed by economically motivated "pseudo-honest" clients. To address this gap, we propose a Reputation-Aware Defense Framework to mitigate strategic behaviors in FL. This framework introduces a multi-dimensional dynamic reputation model that evaluates client behaviors based on gradient alignment, participation consistency, and update stability. The resulting reputation scores are incorporated into both aggregation and incentive mechanisms, forming a behavior-feedback loop that rewards honest participation and penalizes opportunistic strategies. We theoretically prove the convergence of reputation scores, the suppression of low-quality updates in aggregation, and the emergence of honest participation as a Nash equilibrium under the incentive mechanism. Experiments on datasets such as CIFAR-10, FEMNIST, MIMIC-III demonstrate that our approach significantly outperforms baseline methods in accuracy, fairness, and robustness, even when up to 60% of clients act strategically. This study bridges trust modeling and robust optimization in FL, offering a secure foundation for federated systems operating in open and incentive-driven environments. © 2025 by the authors.

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.

The rapid development of consumer electronics technology has greatly promoted the progress of the metaverse. However, as a digitized virtual environment, the metaverse imposes high demands on the security of user identities and assets. In this context, asynchronous remote distributed key generation has become one of the key technologies to ensure the security of the metaverse. Unlike traditional key generation methods, the asynchronous nature makes the distributed key generation process more flexible, helping to address the dynamic and heterogeneous network environment within the metaverse. This paper proposes an asynchronous remote key generation method. This method employs public-key cryptography and basic asynchronous primitives to accomplish remote key generation and exchange through encryption and decryption operations between different nodes. In comparison to traditional methods, the asynchronous remote key generation approach provides higher security and reliability, while also showcasing increased efficiency and flexibility. IEEE

Federated learning (FL) has become an essential enabler of distributed intelligence in cyber-physical-social systems (CPSSs), facilitating decentralized collaboration while upholding data privacy. As CPSS applications increasingly rely on federated models for tasks such as predictive analytics and decision-making, safeguarding the intellectual property of these models has emerged as a pressing concern. To address this, we propose MarkFL, an efficient and easy-to-implement watermarking approach tailored for federated models in CPSS environments. MarkFL enables clients to locally train their models on original tasks while the server simultaneously trains its model on a watermark set. During the weighted averaging phase, a new global model embedded with the watermark is generated. This approach ensures no additional time overhead and offers precise control over its impact on the primary tasks, making MarkFL both efficient and practical for diverse applications. Through experiments on the CIFAR-10 dataset, we demonstrate that MarkFL seamlessly integrates into the FL process while maintaining resilience against watermark removal attacks. To further optimize its performance, we introduced a watermark set generated using minimal training samples, showcasing its potential as a robust and practical solution for real-world FL scenarios.

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 paper presents a federated learning-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. © 2014 IEEE.