Kaiyang Han¹, Xiaoqiang Jia¹, Yangfei Lin², Tsutomu Yoshinaga², Yalong Li², Jiale Wu^2,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2971-2988, 2025, DOI:10.32604/cmc.2025.061563 - 16 April 2025

Abstract With the rapid development of artificial intelligence and the Internet of Things, along with the growing demand for privacy-preserving transmission, the need for efficient and secure communication systems has become increasingly urgent. Traditional communication methods transmit data at the bit level without considering its semantic significance, leading to redundant transmission overhead and reduced efficiency. Semantic communication addresses this issue by extracting and transmitting only the most meaningful semantic information, thereby improving bandwidth efficiency. However, despite reducing the volume of data, it remains vulnerable to privacy risks, as semantic features may still expose sensitive information. To… More >

Aishwarya Rajasekar^*, Vetriselvi Vetrian

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 225-254, 2025, DOI:10.32604/cmes.2025.062642 - 11 April 2025

Abstract The integration of technologies like artificial intelligence, 6G, and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle applications. However, these advancements also generate a surge in data processing requirements, necessitating the offloading of vehicular tasks to edge servers due to the limited computational capacity of vehicles. Despite recent advancements, the robustness and scalability of the existing approaches with respect to the number of vehicles and edge servers and their resources, as well as privacy, remain a concern. In this paper, a lightweight… 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 >

Hyunwoo Park^1,2, Jaedong Lee^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 827-847, 2025, DOI:10.32604/cmes.2025.061178 - 11 April 2025

Abstract Integrating Artificial Intelligence of Things (AIoT) in healthcare offers transformative potential for real-time diagnostics and collaborative learning but presents critical challenges, including privacy preservation, computational efficiency, and regulatory compliance. Traditional approaches, such as differential privacy, homomorphic encryption, and secure multi-party computation, often fail to balance performance and privacy, rendering them unsuitable for resource-constrained healthcare AIoT environments. This paper introduces LMSA (Lightweight Multi-Key Secure Aggregation), a novel framework designed to address these challenges and enable efficient, secure federated learning across distributed healthcare institutions. LMSA incorporates three key innovations: (1) a lightweight multi-key management system leveraging Diffie-Hellman… More >

Yanjin Cheng^1,2, Wenmin Li^1,2,*, Sujuan Qin^1,2, Tengfei Tu^1,2

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4777-4795, 2025, DOI:10.32604/cmc.2025.060380 - 06 March 2025

Abstract Federated learning effectively alleviates privacy and security issues raised by the development of artificial intelligence through a distributed training architecture. Existing research has shown that attackers can compromise user privacy and security by stealing model parameters. Therefore, differential privacy is applied in federated learning to further address malicious issues. However, the addition of noise and the update clipping mechanism in differential privacy jointly limit the further development of federated learning in privacy protection and performance optimization. Therefore, we propose an adaptive adjusted differential privacy federated learning method. First, a dynamic adaptive privacy budget allocation strategy… More >

Hao Li^1,#, Kuan Shao^1,#, Xin Wang², Mufeng Wang³, Zhenyong Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5387-5405, 2025, DOI:10.32604/cmc.2025.059718 - 06 March 2025

Abstract Due to the development of cloud computing and machine learning, users can upload their data to the cloud for machine learning model training. However, dishonest clouds may infer user data, resulting in user data leakage. Previous schemes have achieved secure outsourced computing, but they suffer from low computational accuracy, difficult-to-handle heterogeneous distribution of data from multiple sources, and high computational cost, which result in extremely poor user experience and expensive cloud computing costs. To address the above problems, we propose a multi-precision, multi-sourced, and multi-key outsourcing neural network training scheme. Firstly, we design a multi-precision More >

Yi Zuo, Zhenping Chen^*, Jing Feng, Yunhao Fan

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4649-4667, 2025, DOI:10.32604/cmc.2025.059472 - 06 March 2025

Abstract Image classification is crucial for various applications, including digital construction, smart manufacturing, and medical imaging. Focusing on the inadequate model generalization and data privacy concerns in few-shot image classification, in this paper, we propose a federated learning approach that incorporates privacy-preserving techniques. First, we utilize contrastive learning to train on local few-shot image data and apply various data augmentation methods to expand the sample size, thereby enhancing the model’s generalization capabilities in few-shot contexts. Second, we introduce local differential privacy techniques and weight pruning methods to safeguard model parameters, perturbing the transmitted parameters to ensure More >

Yonghang Yan¹, Xin Xie¹, Hengyi Ren², Ying Cao^1,*, Hongwei Chang³

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5035-5055, 2025, DOI:10.32604/cmc.2025.058276 - 06 March 2025

Abstract Fingerprint features, as unique and stable biometric identifiers, are crucial for identity verification. However, traditional centralized methods of processing these sensitive data linked to personal identity pose significant privacy risks, potentially leading to user data leakage. Federated Learning allows multiple clients to collaboratively train and optimize models without sharing raw data, effectively addressing privacy and security concerns. However, variations in fingerprint data due to factors such as region, ethnicity, sensor quality, and environmental conditions result in significant heterogeneity across clients. This heterogeneity adversely impacts the generalization ability of the global model, limiting its performance across… More >

Rifat Sarker Aoyon¹, Ismail Hossain², M. Abdullah-Al-Wadud³, Jia Uddin^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2617-2640, 2025, DOI:10.32604/cmes.2025.060917 - 03 March 2025

Abstract This research introduces a unique approach to segmenting breast cancer images using a U-Net-based architecture. However, the computational demand for image processing is very high. Therefore, we have conducted this research to build a system that enables image segmentation training with low-power machines. To accomplish this, all data are divided into several segments, each being trained separately. In the case of prediction, the initial output is predicted from each trained model for an input, where the ultimate output is selected based on the pixel-wise majority voting of the expected outputs, which also ensures data privacy.… More >

Bolin Zhang, Ruichun Gu^*, Haiying Liu

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2521-2535, 2025, DOI:10.32604/cmc.2024.060331 - 17 February 2025

Abstract Federated Graph Learning (FGL) enables model training without requiring each client to share local graph data, effectively breaking data silos by aggregating the training parameters from each terminal while safeguarding data privacy. Traditional FGL relies on a centralized server for model aggregation; however, this central server presents challenges such as a single point of failure and high communication overhead. Additionally, efficiently training a robust personalized local model for each client remains a significant objective in federated graph learning. To address these issues, we propose a decentralized Federated Graph Learning framework with efficient communication, termed Decentralized… More >