Rui Wu¹, Yehong Li², Hongjie Guo^3,*, Gangqiang Hu³, Changjun Zhou^3,*, Qile Zou⁴

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083715 - 13 August 2026

Abstract Federated learning (FL) enables collaborative training of deep neural architectures while preserving data privacy, yet its performance often deteriorates in non-IID scenarios, which stems from client-side distribution drift and divergent local updates induced by pervasive data heterogeneity. This challenge is particularly critical for maintaining the structural consistency and generalization of neural models across diverse, distributed sources with significant distribution shifts. In this paper, we investigate how to effectively mitigate label distribution shift and feature distribution skew to enhance the global representation stability of neural architectures. We propose Federated Learning with Consistency Optimization Algorithms (FedCO), a… More >

Malika Abid¹, Mohammed Kamel Benkaddour¹, Mohamed Benouis², Amine Khaldi¹, Monalisa Sahu³, Aditya Kumar Sahu^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.081055 - 27 July 2026

Abstract Federated Learning (FL) is a cutting-edge method in the medical imaging field that allows hospitals to collaboratively build models without revealing patient data. Nevertheless, FL is still vulnerable to numerous security and privacy issues, including, but not limited to, data poisoning, Byzantine attacks, and inference attacks. The existing literature has only partly dealt with this topic by focusing either on particular threats or on mitigation strategies, thus leaving the overall comprehension of the problems and their solutions in medical imaging as inadequate. The main threats to FL are systematically classified in this systematic review, with… More >

Zhuodong Liu¹, Xiangyu Li^2,*, Zhihao Zhang¹

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.085409 - 23 July 2026

Abstract Federated unlearning (FU) enables the removal of specific data contributions from federated learning (FL) models to comply with regulations such as the General Data Protection Regulation (GDPR). However, most existing FU methods are designed for the FedAvg paradigm, where all clients share a single global model. In practice, personalized federated learning (pFL) methods such as FedPer, FedRep, Ditto, and FedBN have become widely adopted due to their superior handling of non-IID data. These methods decompose the model into shared global layers and client-specific personalized layers, fundamentally altering the semantics of unlearning, yet this setting has… More >

Abdulaziz A. Alsulami¹, Qasem Abu Al-Haija^2,*, Rayed Alakhtar³, Ahmad J. Tayeb³, Badraddin Alturki³, Huda Alsobhi⁴, Rayan A. Alsemmeari³

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083321 - 23 July 2026

Abstract The rapid growth of the Internet of Things (IoT) devices has increased the attack area of modern networks, which makes effective intrusion detection systems (IDSs) essential to detect attacks that target IoT infrastructures. Federated learning is a promising approach for collaborative model training in the absence of centralized raw data. Conventional federated approaches rely on fixed client participation and static training configurations, which ensure symmetric treatment of clients despite heterogeneous local data distributions. This can limit convergence and degrade detection performance in non-IID conditions. This paper proposes an Adaptive Action-Based Federated Learning (AA-FL) framework for… 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 >

MengDie Hu^#, Na Wang^*, XueHui Du^#, BaiDong Huang^#, KaiYuan Wang^#

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082658 - 23 July 2026

Abstract With the rapid development of the Internet of Things (IoT) and edge intelligence, the volume of data generated by edge devices has grown explosively. Federated learning (FL), characterized by the paradigm of “data remaining local while models are shared,” has emerged as a key approach for adapting to the distributed architecture of edge computing, breaking down data silos, and enabling privacy preservation. However, its practical deployment in edge computing environments still faces significant challenges, including limited device resources and pronounced data heterogeneity. Existing pruning strategies for federated learning are predominantly based on static and single-design… 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 >

Ulysses Lam^1,*, Jin-Hee Cho², Hyuk Lim³, Terrence Moore⁴, Frederica Free-Nelson⁴, Hyunjae Kang¹, Dan Dongseong Kim¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.084062 - 30 June 2026

Abstract Autonomous vehicles are potentially more vulnerable to cyber-attacks compared to traditional human-driven ones, as they employ electronic sensors to enable self-driving. Cybersecurity for autonomous vehicles will be crucial in the near future. However, intrusion detection systems (IDSes) for vehicles are still in the early stages. Many IDS models that claim to work for vehicles are actually built with traditional Internet datasets rather than those with real vehicle data, which is impractical in reality. In this paper, IDS models are developed with Federated Learning (FL) with the Car-Hacking and CAN-MIRGU datasets, which are obtained from real More >

Arvind Prasad^1,*, Ibrahim Aljubayri², Mohammad Zubair Khan^3,*, Abdulfattah Noorwali⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082021 - 30 June 2026

Abstract Intrusion detection in large-scale IoT deployments becomes particularly challenging during ongoing attack scenarios, where malicious traffic may temporarily dominate benign traffic. In such conditions, streaming network data exhibits severe class imbalance in favor of attack traffic, while device behavior remains heterogeneous, non-identically distributed (non-IID), and temporally evolving. Within federated learning environments, this imbalance can destabilize early aggregation rounds, dominant attack gradients bias the global model, distort decision boundaries, and degrade reliable discrimination of residual benign behavior. Since the server has no access to raw data, these effects can persist across communication rounds if not addressed… 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