Jinhai Wang¹, Junwei Xue¹, Hongyan Zhang², Hui Xiao^3,4, Huiling Wei^3,4, Mingyou Chen^3,4, Jiang Liao², Lufeng Luo^3,4,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1839-1861, 2025, DOI:10.32604/cmc.2025.064489 - 09 June 2025

Abstract Defect detection based on computer vision is a critical component in ensuring the quality of industrial products. However, existing detection methods encounter several challenges in practical applications, including the scarcity of labeled samples, limited adaptability of pre-trained models, and the data heterogeneity in distributed environments. To address these issues, this research proposes an unsupervised defect detection method, FLAME (Federated Learning with Adaptive Multi-Model Embeddings). The method comprises three stages: (1) Feature learning stage: this work proposes FADE (Feature-Adaptive Domain-Specific Embeddings), a framework employs Gaussian noise injection to simulate defective patterns and implements a feature discriminator… More >

Qingyu Tan, Yan Li, Byeong-Seok Shin^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2417-2428, 2025, DOI:10.32604/cmes.2025.063811 - 30 May 2025

Abstract Federated Learning (FL), a practical solution that leverages distributed data across devices without the need for centralized data storage, which enables multiple participants to jointly train models while preserving data privacy and avoiding direct data sharing. Despite its privacy-preserving advantages, FL remains vulnerable to backdoor attacks, where malicious participants introduce backdoors into local models that are then propagated to the global model through the aggregation process. While existing differential privacy defenses have demonstrated effectiveness against backdoor attacks in FL, they often incur a significant degradation in the performance of the aggregated models on benign tasks.… More >

Sulaiman Al Amro^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4623-4658, 2025, DOI:10.32604/cmc.2025.063734 - 19 May 2025

Abstract The rapid proliferation of Internet of Things (IoT) devices has heightened security concerns, making intrusion detection a pivotal challenge in safeguarding these networks. Traditional centralized Intrusion Detection Systems (IDS) often fail to meet the privacy requirements and scalability demands of large-scale IoT ecosystems. To address these challenges, we propose an innovative privacy-preserving approach leveraging Federated Learning (FL) for distributed intrusion detection. Our model eliminates the need for aggregating sensitive data on a central server by training locally on IoT devices and sharing only encrypted model updates, ensuring enhanced privacy and scalability without compromising detection accuracy.… More >

Tianyu Li¹, Dezhi Han¹, Jiatao Li¹, Kuan-Ching Li^2,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4117-4140, 2025, DOI:10.32604/cmc.2025.063630 - 19 May 2025

Abstract As is known, centralized federated learning faces risks of a single point of failure and privacy breaches, and blockchain-based federated learning frameworks can address these challenges to a certain extent in recent works. However, malicious clients may still illegally access the blockchain to upload malicious data or steal on-chain data. In addition, blockchain-based federated training suffers from a heavy storage burden and excessive network communication overhead. To address these issues, we propose an asynchronous, tiered federated learning storage scheme based on blockchain and IPFS. It manages the execution of federated learning tasks through smart contracts… More >

Zhipeng Qin^1,2,*, Hanbing Yan³, Biyang Zhang², Peng Wang², Yitao Li³

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5811-5829, 2025, DOI:10.32604/cmc.2025.063308 - 19 May 2025

Abstract With the widespread adoption of encrypted Domain Name System (DNS) technologies such as DNS over Hyper Text Transfer Protocol Secure (HTTPS), traditional port and protocol-based traffic analysis methods have become ineffective. Although encrypted DNS enhances user privacy protection, it also provides concealed communication channels for malicious software, compelling detection technologies to shift towards statistical feature-based and machine learning approaches. However, these methods still face challenges in real-time performance and privacy protection. This paper proposes a real-time identification technology for encrypted DNS traffic with privacy protection. Firstly, a hierarchical architecture of cloud-edge-end collaboration is designed, incorporating More >

Hayder Faeq Alhashimi¹, Mhd Nour Hindia¹, Kaharudin Dimyati^1,*, Effariza Binti Hanafi¹, Feras Zen Alden², Faizan Qamar³, Quang Ngoc Nguyen^4,5,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3585-3622, 2025, DOI:10.32604/cmc.2025.062867 - 19 May 2025

Abstract The forthcoming 6G wireless networks have great potential for establishing AI-based networks that can enhance end-to-end connection and manage massive data of real-time networks. Artificial Intelligence (AI) advancements have contributed to the development of several innovative technologies by providing sophisticated specific AI mathematical models such as machine learning models, deep learning models, and hybrid models. Furthermore, intelligent resource management allows for self-configuration and autonomous decision-making capabilities of AI methods, which in turn improves the performance of 6G networks. Hence, 6G networks rely substantially on AI methods to manage resources. This paper comprehensively surveys the recent… More >

Rongsen Wu¹, Jie Xu¹, Yuhang Zhang¹, Changming Zhao^2,*, Yiweng Xie³, Zelei Wu¹, Yunji Li², Jinhong Guo⁴, Shiyang Tang^5,6

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4961-4978, 2025, DOI:10.32604/cmc.2025.061396 - 19 May 2025

Abstract With the rapid development of artificial intelligence and Internet of Things technologies, video action recognition technology is widely applied in various scenarios, such as personal life and industrial production. However, while enjoying the convenience brought by this technology, it is crucial to effectively protect the privacy of users’ video data. Therefore, this paper proposes a video action recognition method based on personalized federated learning and spatiotemporal features. Under the framework of federated learning, a video action recognition method leveraging spatiotemporal features is designed. For the local spatiotemporal features of the video, a new differential information… More >

Pikkili Gopala Krishna^1,*, Jalari Somasekar²

Intelligent Automation & Soft Computing, Vol.40, pp. 235-259, 2025, DOI:10.32604/iasc.2025.063657 - 28 April 2025

Abstract Stress is mental tension caused by difficult situations, often experienced by hospital workers and IT professionals who work long hours. It is essential to detect the stress in shift workers to improve their health. However, existing models measure stress with physiological signals such as PPG, EDA, and blink data, which could not identify the stress level accurately. Additionally, the works face challenges with limited data, inefficient spatial relationships, security issues with health data, and long-range temporal dependencies. In this paper, we have developed a federated learning-based stress detection system for IT and hospital workers, integrating… More >

Sony Peng¹, Sophort Siet¹, Ilkhomjon Sadriddinov¹, Dae-Young Kim^2,*, Kyuwon Park^3,*, Doo-Soon Park²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2041-2057, 2025, DOI:10.32604/cmc.2025.061166 - 16 April 2025

Abstract Recommendation systems (RSs) are crucial in personalizing user experiences in digital environments by suggesting relevant content or items. Collaborative filtering (CF) is a widely used personalization technique that leverages user-item interactions to generate recommendations. However, it struggles with challenges like the cold-start problem, scalability issues, and data sparsity. To address these limitations, we develop a Graph Convolutional Networks (GCNs) model that captures the complex network of interactions between users and items, identifying subtle patterns that traditional methods may overlook. We integrate this GCNs model into a federated learning (FL) framework, enabling the model to learn… More >

Saad Alahmari^1,*, Abdulwhab Alkharashi²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 849-873, 2025, DOI:10.32604/cmes.2025.062549 - 11 April 2025

Abstract The Internet of Things (IoT) is emerging as an innovative phenomenon concerned with the development of numerous vital applications. With the development of IoT devices, huge amounts of information, including users’ private data, are generated. IoT systems face major security and data privacy challenges owing to their integral features such as scalability, resource constraints, and heterogeneity. These challenges are intensified by the fact that IoT technology frequently gathers and conveys complex data, creating an attractive opportunity for cyberattacks. To address these challenges, artificial intelligence (AI) techniques, such as machine learning (ML) and deep learning (DL),… More >