Yongfeng Zhang^1,*, Jie Chen^2,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.088561 - 15 September 2026

Abstract Speaker verification on phones, wearables, and voice-enabled Internet-of-Things gateways must balance local privacy with reliable decisions under adverse audio. Existing privacy-preserving verification schemes generally protect a fixed representation, whereas edge-offloading policies usually adapt computation without attaching an explicit feature-disclosure budget; neither line alone coordinates trial uncertainty, communication state, and privacy expenditure. This paper presents privacy-preserving uncertainty-aware adaptive feature offloading (P-UAFO), an edge-intelligence framework that keeps raw audio local and transmits only clipped, projected, quantized, and Gaussian-perturbed intermediate features when their expected benefit justifies resource cost. Its online pipeline first estimates decision uncertainty and resource state,… More >

Fangfang Shan^*, Yuhang Liu^*, Lulu Fan, Zhuo Chen, Yifan Mao, Peixue Wang

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085467 - 15 September 2026

Abstract The engineering of Federated Learning (FL) systems faces significant challenges in balancing two critical non-functional requirements: ensuring robust system utility and maintaining high privacy protection standards under non-independent and identically distributed (Non-IID) data environments. Existing software architectures often struggle to achieve an optimal trade-off between these competing demands. This paper proposes SAM-ADPFL, a novel architectural framework designed to improve the engineering and management of privacy-preserving distributed machine learning systems. First, we design a geometry-aware adaptive aggregation component that dynamically reallocates aggregation weights based on local landscape properties, guiding the global model to effectively suppress model More >

Jie Li, Fuyuan Song^*, Qin Jiang

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085946 - 13 August 2026

Abstract Mobile crowdsensing enables large-scale sensing tasks through smart devices carried by users and has been widely applied in intelligent transportation and environmental monitoring. With the increasing complexity of sensing tasks, many tasks require the collaboration of multiple workers with different skills. However, both task-required skills and worker skills are privacy-sensitive, and directly exposing them to the platform may reveal task intentions and workers’ capability profiles. To address this issue, this paper proposes Dual-Fog Privacy-Preserving Multi-skill Task Allocation (DPMTA), a privacy-preserving task allocation scheme for multi-skill collaborative tasks. DPMTA adopts a dual-fog architecture to separately protect… More >

Sofia Terzi^1,2,*, Katerina Zourou³, Ioannis Stamelos¹, Konstantinos Votis⁴

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084524 - 13 August 2026

Abstract Higher Education (HE) institutions and Lifelong Learning (LLL) providers increasingly issue digital certificates, yet prevailing solutions often lack interoperable credential schemas, verifiable provenance, and privacy-preserving verification at scale. In parallel, European initiatives promote verifiable credentials and cross-border recognition, but there is limited evidence on how Hyperledger Indy components—Redundant Byzantine Fault Tolerance (RBFT) consensus, Decentralized Identifiers (DIDs), Anonymous Credentials (AnonCreds), and revocation registries—can be integrated into existing learning platforms while satisfying software service-quality and governance requirements. This paper presents a permissioned, privacy-preserving blockchain architecture for secure issuance and verification of educational verifiable credentials (VCs) and evaluates… More >

Tallha Akram^1,*, Sadiq Ahmad^2,*, Meshal Alharbi³

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.081458 - 13 August 2026

Abstract Scalability limitations, privacy risks, and lack of adaptability remain key challenges in centralized medical digital win (MDT) architectures. While federated learning (FL) mitigates the need to share raw data, it often lacks adaptability to dynamic clinical environments and does not fully integrate formal privacy guarantees into the learning process. To address these challenges, this paper proposes a decentralized, federated, multi-agent reinforcement learning (F-MARL) framework to coordinate MDTs in the presence of partial observability. The framework is formulated as a multi-agent partially observable Markov decision process (MA-POMDP), enabling distributed policy optimization in heterogeneous and uncertain clinical… More >

Hongzhen Liu¹, Liang Xie¹, Zhiqiang Ru^2,*, Yuan Wan¹, Zhe Zhang¹, Xi Fang^1,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082755 - 23 July 2026

Abstract Federated learning is a privacy-preserving machine learning framework that facilitates model training directly on decentralized data that, due to privacy concerns or transmission costs, cannot be centralized on a server for traditional model training. To prevent adversaries from reconstructing the original data via parameters transmitted during the process, homomorphic encryption is a commonly adopted method. However, it introduces significant communication and computation costs and risks total security failure if any secret key is compromised. This paper proposes a privacy-preserving aggregation mechanism that enables each client to independently generate partial keys for encryption while allowing decryption… More >

Qiuhao Xu^1,2, Chen Wang^1,3,*, Xi Wen¹, Lurong Jiang¹, Wenying Zheng^4,*, Zhengkui Chen¹

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082310 - 23 July 2026

Abstract The convergence of Generative Artificial Intelligence and biometric recognition is reshaping modern healthcare. It enables more adaptive and intelligent human–machine interactions. Epilepsy, a common neurological disorder affecting millions worldwide, relies heavily on electroencephalography (EEG) signals for diagnosis and monitoring. Wearable consumer devices with EEG sensors support continuous physiological data collection. However, transmitting sensitive biometric data to centralized servers introduces serious privacy and security risks. Federated learning (FL) provides a distributed training framework that keeps raw data on local devices. Despite this advantage, existing FL methods remain vulnerable to gradient leakage attacks, where adversaries may infer More >

Lilia Tightiz¹, L. Minh Dang^2,3, Hyosik Yang^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081849 - 30 June 2026

Abstract We present QFedFormer, a federated transformer for dynamic electric vehicle (EV)-charging price prediction that combines quantization-aware training, SHAP-guided explainability, and blockchain-based incentives. The framework trains across distributed charging stations without centralizing user data, and programmable contracts set tariffs from forecasted demand and user-declared flexibility, while token rewards are derived from SHAP-based utility scores and anchored on-chain via Merkle proofs. On a real-world dataset, QFedFormer attains an energy-demand RMSE of 1.82±0.02 kWh and a tariff RMSE of 11.83±0.10 KRW/kWh (MAPE 2.7±0.2%) in the non-private baseline, outperforming FedAvg and Block-FeDL by 14.1% and 9.5More > Graphic Abstract

M. Saad Bin Ilyas¹, Sohail Masood Bhatti¹, Ghazanfar Latif^2,*, Sherif Abdelhamid³, Arfan Jaffar¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081720 - 30 June 2026

Abstract Federated learning (FL) has emerged as a promising approach for enabling collaborative model training across distributed Internet of Medical Things (IoMT) devices without sharing sensitive data. Existing FL frameworks face significant challenges in healthcare settings, including vulnerability to adversarial attacks, lack of verifiable update integrity, and limited robustness under heterogeneous data distributions. These limitations hinder reliable deployment in critical medical applications. To address these challenges, this paper proposes RP-IoMT, a robust and privacy-preserving FL framework that integrates secure multi-party computation (MPC), zero-knowledge proof-based gradient verification, and robust aggregation mechanisms. The objective of this work is… More >

Karim Gasmi^1,*, Afrah Alanazi², Inam Alanazi², Sahar Almenwer¹, Norah Alanazi¹, Sarah Almaghrabi³, Samia Yahyaoui⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081705 - 30 June 2026

Abstract Cardiac magnetic resonance imaging (MRI) segmentation is an essential aspect of quantitative cardiovascular analysis, facilitating accurate evaluation of ventricular volumes, myocardial mass, and functional parameters. Deep learning-based segmentation models have shown strong performance on benchmark datasets such as ACDC, but they remain challenging to deploy in real-world multi-centre settings. Data privacy laws make it hard to share data across institutions, and differences in imaging protocols and patient populations mean that data is not always distributed in the same way (non-IID). This can have a big impact on how well models work together and how well… More >