Danping Niu¹, Yuan Ping^1,*, Chun Guo², Xiaojun Wang³, Bin Hao⁴

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073630 - 12 January 2026

Abstract With the increasing complexity of malware attack techniques, traditional detection methods face significant challenges, such as privacy preservation, data heterogeneity, and lacking category information. To address these issues, we propose Federated Dynamic Prototype Learning (FedDPL) for malware classification by integrating Federated Learning with a specifically designed K-means. Under the Federated Learning framework, model training occurs locally without data sharing, effectively protecting user data privacy and preventing the leakage of sensitive information. Furthermore, to tackle the challenges of data heterogeneity and the lack of category information, FedDPL introduces a dynamic prototype learning mechanism, which adaptively adjusts the More >

Hang Wen^1,2, Kai Zeng^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072909 - 12 January 2026

Abstract Federated learning often experiences slow and unstable convergence due to edge-side data heterogeneity. This problem becomes more severe when edge participation rate is low, as the information collected from different edge devices varies significantly. As a result, communication overhead increases, which further slows down the convergence process. To address this challenge, we propose a simple yet effective federated learning framework that improves consistency among edge devices. The core idea is clusters the lookahead gradients collected from edge devices on the cloud server to obtain personalized momentum for steering local updates. In parallel, a global momentum… More > Graphic Abstract

Haotian Wu¹, Jiaming Pei², Jinhai Li^3,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.069873 - 10 November 2025

Abstract With the increasing complexity of vehicular networks and the proliferation of connected vehicles, Federated Learning (FL) has emerged as a critical framework for decentralized model training while preserving data privacy. However, efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging. To address these issues, we propose Federated Learning with Client Selection and Adaptive Weighting (FedCW), a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks. FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts 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 >

Long Cai, Ke Gu^*, Jiaqi Lei

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 239-258, 2025, DOI:10.32604/cmc.2025.061377 - 26 March 2025

Abstract Large-scale neural networks-based federated learning (FL) has gained public recognition for its effective capabilities in distributed training. Nonetheless, the open system architecture inherent to federated learning systems raises concerns regarding their vulnerability to potential attacks. Poisoning attacks turn into a major menace to federated learning on account of their concealed property and potent destructive force. By altering the local model during routine machine learning training, attackers can easily contaminate the global model. Traditional detection and aggregation solutions mitigate certain threats, but they are still insufficient to completely eliminate the influence generated by attackers. Therefore, federated… More >

Jianfeng Lu¹, Tao Huang¹, Yuanai Xie^2,*, Shuqin Cao¹, Bing Li³

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 619-634, 2025, DOI:10.32604/cmc.2025.060094 - 26 March 2025

Abstract The proliferation of deep learning (DL) has amplified the demand for processing large and complex datasets for tasks such as modeling, classification, and identification. However, traditional DL methods compromise client privacy by collecting sensitive data, underscoring the necessity for privacy-preserving solutions like Federated Learning (FL). FL effectively addresses escalating privacy concerns by facilitating collaborative model training without necessitating the sharing of raw data. Given that FL clients autonomously manage training data, encouraging client engagement is pivotal for successful model training. To overcome challenges like unreliable communication and budget constraints, we present ENTIRE, a contract-based dynamic… More >

Xiaojun Jin¹, Chao Ma^2,*, Song Luo², Pengyi Zeng¹, Yifei Wei¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2317-2336, 2024, DOI:10.32604/cmc.2024.055344 - 18 November 2024

Abstract Federated learning enables data owners in the Internet of Things (IoT) to collaborate in training models without sharing private data, creating new business opportunities for building a data market. However, in practical operation, there are still some problems with federated learning applications. Blockchain has the characteristics of decentralization, distribution, and security. The blockchain-enabled federated learning further improve the security and performance of model training, while also expanding the application scope of federated learning. Blockchain has natural financial attributes that help establish a federated learning data market. However, the data of federated learning tasks may be… More >

Mohammad Z. Masoud¹, Yousef Jaradat¹, Ahmad Manasrah², Mohammad Alia³, Khaled Suwais^4,*, Sally Almanasra⁴

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 515-532, 2024, DOI:10.32604/cmc.2023.044504 - 30 January 2024

Abstract This work carried out a measurement study of the Ethereum Peer-to-Peer (P2P) network to gain a better understanding of the underlying nodes. Ethereum was applied because it pioneered distributed applications, smart contracts, and Web3. Moreover, its application layer language “Solidity” is widely used in smart contracts across different public and private blockchains. To this end, we wrote a new Ethereum client based on Geth to collect Ethereum node information. Moreover, various web scrapers have been written to collect nodes’ historical data from the Internet Archive and the Wayback Machine project. The collected data has been… More >

Hongle Guo^1,2, Yingchi Mao^1,2,*, Xiaoming He^1,2, Benteng Zhang^1,2, Tianfu Pang^1,2, Ping Ping^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 383-403, 2024, DOI:10.32604/cmes.2023.031466 - 30 December 2023

Abstract Data sharing and privacy protection are made possible by federated learning, which allows for continuous model parameter sharing between several clients and a central server. Multiple reliable and high-quality clients must participate in practical applications for the federated learning global model to be accurate, but because the clients are independent, the central server cannot fully control their behavior. The central server has no way of knowing the correctness of the model parameters provided by each client in this round, so clients may purposefully or unwittingly submit anomalous data, leading to abnormal behavior, such as becoming… More >

Tariq Qayyum¹, Zouheir Trabelsi^1,*, Asadullah Tariq¹, Muhammad Ali², Kadhim Hayawi³, Irfan Ud Din⁴

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1739-1757, 2023, DOI:10.32604/cmc.2023.043684 - 29 November 2023

Abstract Federated Learning (FL) enables collaborative and privacy-preserving training of machine learning models within the Internet of Vehicles (IoV) realm. While FL effectively tackles privacy concerns, it also imposes significant resource requirements. In traditional FL, trained models are transmitted to a central server for global aggregation, typically in the cloud. This approach often leads to network congestion and bandwidth limitations when numerous devices communicate with the same server. The need for Flexible Global Aggregation and Dynamic Client Selection in FL for the IoV arises from the inherent characteristics of IoV environments. These include diverse and distributed… More >