Faisal Mahmud¹, Fahim Mahmud², Rashedur M. Rahman^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073519 - 12 January 2026

Abstract Federated Learning (FL) has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data, making it suitable for privacy-sensitive applications such as healthcare, finance, and smart systems. As the field continues to evolve, the research field has become more complex and scattered, covering different system designs, training methods, and privacy techniques. This survey is organized around the three core challenges: how the data is distributed, how models are synchronized, and how to defend against attacks. It provides a structured and up-to-date review of… More >

Bello Musa Yakubu^1,*, Nor Shahida Mohd Jamail ², Rabia Latif ², Seemab Latif ³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072426 - 12 January 2026

Abstract Federated Learning (FL) enables joint training over distributed devices without data exchange but is highly vulnerable to attacks by adversaries in the form of model poisoning and malicious update injection. This work proposes Secured-FL, a blockchain-based defensive framework that combines smart contract–based authentication, clustering-driven outlier elimination, and dynamic threshold adjustment to defend against adversarial attacks. The framework was implemented on a private Ethereum network with a Proof-of-Authority consensus algorithm to ensure tamper-resistant and auditable model updates. Large-scale simulation on the Cyber Data dataset, under up to 50% malicious client settings, demonstrates Secured-FL achieves 6%–12% higher accuracy, More >

Farhad Mortezapour Shiri^*, Thinagaran Perumal, Norwati Mustapha, Raihani Mohamed

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1389-1485, 2025, DOI:10.32604/cmes.2025.071858 - 26 November 2025

Abstract Human Activity Recognition (HAR) represents a rapidly advancing research domain, propelled by continuous developments in sensor technologies and the Internet of Things (IoT). Deep learning has become the dominant paradigm in sensor-based HAR systems, offering significant advantages over traditional machine learning methods by eliminating manual feature extraction, enhancing recognition accuracy for complex activities, and enabling the exploitation of unlabeled data through generative models. This paper provides a comprehensive review of recent advancements and emerging trends in deep learning models developed for sensor-based human activity recognition (HAR) systems. We begin with an overview of fundamental HAR… More > Graphic Abstract

Imran Ahmed^1,#, Misbah Ahmad^2,3,#, Gwanggil Jeon^4,5,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4237-4273, 2025, DOI:10.32604/cmc.2025.068319 - 23 October 2025

Abstract The rapid convergence of Information and Communication Technologies (ICT), driven by advancements in 5G/6G networks, cloud computing, Artificial Intelligence (AI), and the Internet of Things (IoT), is reshaping modern digital ecosystems. As massive, distributed data streams are generated across edge devices and network layers, there is a growing need for intelligent, privacy-preserving AI solutions that can operate efficiently at the network edge. Federated Learning (FL) enables decentralized model training without transferring sensitive data, addressing key challenges around privacy, bandwidth, and latency. Despite its benefits in enhancing efficiency, real-time analytics, and regulatory compliance, FL adoption faces… More >

Mohammed Naif Alatawi^*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 695-727, 2025, DOI:10.32604/cmc.2025.066606 - 29 August 2025

Abstract Adaptive robust secure framework plays a vital role in implementing intelligent automation and decentralized decision making of Industry 5.0. Latency, privacy risks and the complexity of industrial networks have been preventing attempts at traditional cloud-based learning systems. We demonstrate that, to overcome these challenges, for instance, the EdgeGuard-IoT framework, a 6G edge intelligence framework enhancing cybersecurity and operational resilience of the smart grid, is needed on the edge to integrate Secure Federated Learning (SFL) and Adaptive Anomaly Detection (AAD). With ultra-reliable low latency communication (URLLC) of 6G, artificial intelligence-based network orchestration, and massive machine type… 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 >

Xiangmin Guo^1,2, Guangjun Liang^1,2,*, Jiayin Liu^1,2, Xianyi Chen^3,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3305-3323, 2023, DOI:10.32604/cmc.2023.044529 - 26 December 2023

Abstract Cloud storage is widely used by large companies to store vast amounts of data and files, offering flexibility, financial savings, and security. However, information shoplifting poses significant threats, potentially leading to poor performance and privacy breaches. Blockchain-based cognitive computing can help protect and maintain information security and privacy in cloud platforms, ensuring businesses can focus on business development. To ensure data security in cloud platforms, this research proposed a blockchain-based Hybridized Data Driven Cognitive Computing (HD2C) model. However, the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of… More >