Peizheng Lai¹, Minqing Zhang^1,2,*, Yixin Tang¹, Ya Yue¹, Fuqiang Di^1,2

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2935-2957, 2025, DOI:10.32604/cmc.2025.065887 - 03 July 2025

Abstract Federated Learning (FL) has emerged as a promising distributed machine learning paradigm that enables multi-party collaborative training while eliminating the need for raw data sharing. However, its reliance on a server introduces critical security vulnerabilities: malicious servers can infer private information from received local model updates or deliberately manipulate aggregation results. Consequently, achieving verifiable aggregation without compromising client privacy remains a critical challenge. To address these problem, we propose a reversible data hiding in encrypted domains (RDHED) scheme, which designs joint secret message embedding and extraction mechanism. This approach enables clients to embed secret messages… More >

Yunong Yang¹, Long Ma¹, Liang Fan², Tao Xie^3,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3459-3476, 2025, DOI:10.32604/cmc.2025.064103 - 03 July 2025

Abstract Federated learning has emerged as an essential technique of protecting privacy since it allows clients to train models locally without explicitly exchanging sensitive data. Extensive research has been conducted on the issue of data heterogeneity in federated learning, but effective model training with severely imbalanced label distributions remains an unexplored area. This paper presents a novel Cluster Federated Learning Algorithm with Intra-cluster Correction (CFIC). First, CFIC selects samples from each cluster during each round of sampling, ensuring that no single category of data dominates the model training. Second, in addition to updating local models, CFIC… More >

Omar Alnajar^*, Ahmed Barnawi

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3861-3897, 2025, DOI:10.32604/cmes.2025.065833 - 30 June 2025

Abstract The Tactile Internet of Things (TIoT) promises transformative applications—ranging from remote surgery to industrial robotics—by incorporating haptic feedback into traditional IoT systems. Yet TIoT’s stringent requirements for ultra-low latency, high reliability, and robust privacy present significant challenges. Conventional centralized Federated Learning (FL) architectures struggle with latency and privacy constraints, while fully distributed FL (DFL) faces scalability and non-IID data issues as client populations expand and datasets become increasingly heterogeneous. To address these limitations, we propose a Clustered Distributed Federated Learning (CDFL) architecture tailored for a 6G-enabled TIoT environment. Clients are grouped into clusters based on… More >

Ammar Odeh^*, Anas Abu Taleb

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 447-461, 2025, DOI:10.32604/cmc.2025.065426 - 09 June 2025

Abstract The increasing deployment of Internet of Things (IoT) devices has introduced significant security challenges, including identity spoofing, unauthorized access, and data integrity breaches. Traditional security mechanisms rely on centralized frameworks that suffer from single points of failure, scalability issues, and inefficiencies in real-time security enforcement. To address these limitations, this study proposes the Blockchain-Enhanced Trust and Access Control for IoT Security (BETAC-IoT) model, which integrates blockchain technology, smart contracts, federated learning, and Merkle tree-based integrity verification to enhance IoT security. The proposed model eliminates reliance on centralized authentication by employing decentralized identity management, ensuring tamper-proof… More >

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 >