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  • Open Access

    ARTICLE

    Improving Federated Learning through Abnormal Client Detection and Incentive

    Hongle Guo1,2, Yingchi Mao1,2,*, Xiaoming He1,2, Benteng Zhang1,2, Tianfu Pang1,2, Ping Ping1,2

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

    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 >

  • Open Access

    ARTICLE

    Blockchain-Based Cognitive Computing Model for Data Security on a Cloud Platform

    Xiangmin Guo1,2, Guangjun Liang1,2,*, Jiayin Liu1,2, Xianyi Chen3,*

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

    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 >

  • Open Access

    REVIEW

    Multi-Robot Privacy-Preserving Algorithms Based on Federated Learning: A Review

    Jiansheng Peng1,2,*, Jinsong Guo1, Fengbo Bao1, Chengjun Yang2, Yong Xu2, Yong Qin2

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2971-2994, 2023, DOI:10.32604/cmc.2023.041897

    Abstract The robotics industry has seen rapid development in recent years due to the Corona Virus Disease 2019. With the development of sensors and smart devices, factories and enterprises have accumulated a large amount of data in their daily production, which creates extremely favorable conditions for robots to perform machine learning. However, in recent years, people’s awareness of data privacy has been increasing, leading to the inability to circulate data between different enterprises, resulting in the emergence of data silos. The emergence of federated learning provides a feasible solution to this problem, and the combination of… More >

  • Open Access

    ARTICLE

    Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

    Chunlin Xiong1,2, Zhengqiu Weng3,4,*, Jia Liu1, Liang Gu2, Fayez Alqahtani5, Amr Gafar6, Pradip Kumar Sharma7

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 3013-3032, 2024, DOI:10.32604/cmes.2023.031088

    Abstract With the development of hardware devices and the upgrading of smartphones, a large number of users save privacy-related information in mobile devices, mainly smartphones, which puts forward higher demands on the protection of mobile users’ privacy information. At present, mobile user authentication methods based on human-computer interaction have been extensively studied due to their advantages of high precision and non-perception, but there are still shortcomings such as low data collection efficiency, untrustworthy participating nodes, and lack of practicability. To this end, this paper proposes a privacy-enhanced mobile user authentication method with motion sensors, which mainly… More >

  • Open Access

    ARTICLE

    Real-Time Prediction Algorithm for Intelligent Edge Networks with Federated Learning-Based Modeling

    Seungwoo Kang1, Seyha Ros1, Inseok Song1, Prohim Tam1, Sa Math2, Seokhoon Kim1,3,*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1967-1983, 2023, DOI:10.32604/cmc.2023.045020

    Abstract Intelligent healthcare networks represent a significant component in digital applications, where the requirements hold within quality-of-service (QoS) reliability and safeguarding privacy. This paper addresses these requirements through the integration of enabler paradigms, including federated learning (FL), cloud/edge computing, software-defined/virtualized networking infrastructure, and converged prediction algorithms. The study focuses on achieving reliability and efficiency in real-time prediction models, which depend on the interaction flows and network topology. In response to these challenges, we introduce a modified version of federated logistic regression (FLR) that takes into account convergence latencies and the accuracy of the final FL model… More >

  • Open Access

    ARTICLE

    Flexible Global Aggregation and Dynamic Client Selection for Federated Learning in Internet of Vehicles

    Tariq Qayyum1, Zouheir Trabelsi1,*, 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

    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 >

  • Open Access

    ARTICLE

    A Differential Privacy Federated Learning Scheme Based on Adaptive Gaussian Noise

    Sanxiu Jiao1, Lecai Cai2,*, Xinjie Wang1, Kui Cheng2, Xiang Gao3

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1679-1694, 2024, DOI:10.32604/cmes.2023.030512

    Abstract As a distributed machine learning method, federated learning (FL) has the advantage of naturally protecting data privacy. It keeps data locally and trains local models through local data to protect the privacy of local data. The federated learning method effectively solves the problem of artificial Smart data islands and privacy protection issues. However, existing research shows that attackers may still steal user information by analyzing the parameters in the federated learning training process and the aggregation parameters on the server side. To solve this problem, differential privacy (DP) techniques are widely used for privacy protection… More > Graphic Abstract

    A Differential Privacy Federated Learning Scheme Based on Adaptive Gaussian Noise

  • Open Access

    ARTICLE

    Decentralized Heterogeneous Federal Distillation Learning Based on Blockchain

    Hong Zhu*, Lisha Gao, Yitian Sha, Nan Xiang, Yue Wu, Shuo Han

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3363-3377, 2023, DOI:10.32604/cmc.2023.040731

    Abstract Load forecasting is a crucial aspect of intelligent Virtual Power Plant (VPP) management and a means of balancing the relationship between distributed power grids and traditional power grids. However, due to the continuous emergence of power consumption peaks, the power supply quality of the power grid cannot be guaranteed. Therefore, an intelligent calculation method is required to effectively predict the load, enabling better power grid dispatching and ensuring the stable operation of the power grid. This paper proposes a decentralized heterogeneous federated distillation learning algorithm (DHFDL) to promote trusted federated learning (FL) between different federates… More >

  • Open Access

    ARTICLE

    A Blockchain-Assisted Distributed Edge Intelligence for Privacy-Preserving Vehicular Networks

    Muhammad Firdaus1, Harashta Tatimma Larasati2, Kyung-Hyune Rhee3,*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2959-2978, 2023, DOI:10.32604/cmc.2023.039487

    Abstract The enormous volume of heterogeneous data from various smart device-based applications has growingly increased a deeply interlaced cyber-physical system. In order to deliver smart cloud services that require low latency with strong computational processing capabilities, the Edge Intelligence System (EIS) idea is now being employed, which takes advantage of Artificial Intelligence (AI) and Edge Computing Technology (ECT). Thus, EIS presents a potential approach to enforcing future Intelligent Transportation Systems (ITS), particularly within a context of a Vehicular Network (VNets). However, the current EIS framework meets some issues and is conceivably vulnerable to multiple adversarial attacks… More >

  • Open Access

    ARTICLE

    FedTC: A Personalized Federated Learning Method with Two Classifiers

    Yang Liu1,3, Jiabo Wang1,2,*, Qinbo Liu1, Mehdi Gheisari1, Wanyin Xu1, Zoe L. Jiang1, Jiajia Zhang1,*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3013-3027, 2023, DOI:10.32604/cmc.2023.039452

    Abstract Centralized training of deep learning models poses privacy risks that hinder their deployment. Federated learning (FL) has emerged as a solution to address these risks, allowing multiple clients to train deep learning models collaboratively without sharing raw data. However, FL is vulnerable to the impact of heterogeneous distributed data, which weakens convergence stability and suboptimal performance of the trained model on local data. This is due to the discarding of the old local model at each round of training, which results in the loss of personalized information in the model critical for maintaining model accuracy… More >

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