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  • Open Access

    ARTICLE

    A Double-Compensation-Based Federated Learning Scheme for Data Privacy Protection in a Social IoT Scenario

    Junqi Guo1,2, Qingyun Xiong1,*, Minghui Yang1, Ziyun Zhao1

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 827-848, 2023, DOI:10.32604/cmc.2023.036450

    Abstract Nowadays, smart wearable devices are used widely in the Social Internet of Things (IoT), which record human physiological data in real time. To protect the data privacy of smart devices, researchers pay more attention to federated learning. Although the data leakage problem is somewhat solved, a new challenge has emerged. Asynchronous federated learning shortens the convergence time, while it has time delay and data heterogeneity problems. Both of the two problems harm the accuracy. To overcome these issues, we propose an asynchronous federated learning scheme based on double compensation to solve the problem of time… More >

  • Open Access

    ARTICLE

    MSEs Credit Risk Assessment Model Based on Federated Learning and Feature Selection

    Zhanyang Xu1, Jianchun Cheng1,*, Luofei Cheng1, Xiaolong Xu1,2, Muhammad Bilal3

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5573-5595, 2023, DOI:10.32604/cmc.2023.037287

    Abstract Federated learning has been used extensively in business innovation scenarios in various industries. This research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment scenario. First, this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises (MSEs) using multi-dimensional enterprise data and multi-perspective enterprise information. The proposed model includes four main processes: namely encrypted entity alignment, hybrid feature selection, secure multi-party computation, and global model updating. Secondly, a two-step feature selection algorithm based… More >

  • Open Access

    ARTICLE

    A Client Selection Method Based on Loss Function Optimization for Federated Learning

    Yan Zeng1,2,3, Siyuan Teng1, Tian Xiang4,*, Jilin Zhang1,2,3, Yuankai Mu5, Yongjian Ren1,2,3,*, Jian Wan1,2,3

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 1047-1064, 2023, DOI:10.32604/cmes.2023.027226

    Abstract Federated learning is a distributed machine learning method that can solve the increasingly serious problem of data islands and user data privacy, as it allows training data to be kept locally and not shared with other users. It trains a global model by aggregating locally-computed models of clients rather than their raw data. However, the divergence of local models caused by data heterogeneity of different clients may lead to slow convergence of the global model. For this problem, we focus on the client selection with federated learning, which can affect the convergence performance of the… More > Graphic Abstract

    A Client Selection Method Based on Loss Function Optimization for Federated Learning

  • Open Access

    ARTICLE

    Residential Energy Consumption Forecasting Based on Federated Reinforcement Learning with Data Privacy Protection

    You Lu1,2,#,*, Linqian Cui1,2,#,*, Yunzhe Wang1,2, Jiacheng Sun1,2, Lanhui Liu3

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 717-732, 2023, DOI:10.32604/cmes.2023.027032

    Abstract Most studies have conducted experiments on predicting energy consumption by integrating data for model training. However, the process of centralizing data can cause problems of data leakage. Meanwhile, many laws and regulations on data security and privacy have been enacted, making it difficult to centralize data, which can lead to a data silo problem. Thus, to train the model while maintaining user privacy, we adopt a federated learning framework. However, in all classical federated learning frameworks secure aggregation, the Federated Averaging (FedAvg) method is used to directly weight the model parameters on average, which may… More >

  • Open Access

    ARTICLE

    Vertical Federated Learning Based on Consortium Blockchain for Data Sharing in Mobile Edge Computing

    Yonghao Zhang1,3, Yongtang Wu2, Tao Li1, Hui Zhou1,3, Yuling Chen1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 345-361, 2023, DOI:10.32604/cmes.2023.026920

    Abstract The data in Mobile Edge Computing (MEC) contains tremendous market value, and data sharing can maximize the usefulness of the data. However, certain data is quite sensitive, and sharing it directly may violate privacy. Vertical Federated Learning (VFL) is a secure distributed machine learning framework that completes joint model training by passing encrypted model parameters rather than raw data, so there is no data privacy leakage during the training process. Therefore, the VFL can build a bridge between data demander and owner to realize data sharing while protecting data privacy. Typically, the VFL requires a… More >

  • Open Access

    ARTICLE

    COVID-19 Classification from X-Ray Images: An Approach to Implement Federated Learning on Decentralized Dataset

    Ali Akbar Siddique1, S. M. Umar Talha1, M. Aamir1, Abeer D. Algarni2, Naglaa F. Soliman2,*, Walid El-Shafai3,4

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3883-3901, 2023, DOI:10.32604/cmc.2023.037413

    Abstract The COVID-19 pandemic has devastated our daily lives, leaving horrific repercussions in its aftermath. Due to its rapid spread, it was quite difficult for medical personnel to diagnose it in such a big quantity. Patients who test positive for Covid-19 are diagnosed via a nasal PCR test. In comparison, polymerase chain reaction (PCR) findings take a few hours to a few days. The PCR test is expensive, although the government may bear expenses in certain places. Furthermore, subsets of the population resist invasive testing like swabs. Therefore, chest X-rays or Computerized Vomography (CT) scans are… More >

  • Open Access

    ARTICLE

    A Secure and Effective Energy-Aware Fixed-Point Quantization Scheme for Asynchronous Federated Learning

    Zerui Zhen1, Zihao Wu2, Lei Feng1,*, Wenjing Li1, Feng Qi1, Shixuan Guo1

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2939-2955, 2023, DOI:10.32604/cmc.2023.036505

    Abstract Asynchronous federated learning (AsynFL) can effectively mitigate the impact of heterogeneity of edge nodes on joint training while satisfying participant user privacy protection and data security. However, the frequent exchange of massive data can lead to excess communication overhead between edge and central nodes regardless of whether the federated learning (FL) algorithm uses synchronous or asynchronous aggregation. Therefore, there is an urgent need for a method that can simultaneously take into account device heterogeneity and edge node energy consumption reduction. This paper proposes a novel Fixed-point Asynchronous Federated Learning (FixedAsynFL) algorithm, which could mitigate the… More >

  • Open Access

    ARTICLE

    PoQ-Consensus Based Private Electricity Consumption Forecasting via Federated Learning

    Yiqun Zhu1, Shuxian Sun1, Chunyu Liu1, Xinyi Tian1, Jingyi He2, Shuai Xiao2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 3285-3297, 2023, DOI:10.32604/cmes.2023.026691

    Abstract With the rapid development of artificial intelligence and computer technology, grid corporations have also begun to move towards comprehensive intelligence and informatization. However, data-based informatization can bring about the risk of privacy exposure of fine-grained information such as electricity consumption data. The modeling of electricity consumption data can help grid corporations to have a more thorough understanding of users’ needs and their habits, providing better services for users. Nevertheless, users’ electricity consumption data is sensitive and private. In order to achieve highly efficient analysis of massive private electricity consumption data without direct access, a blockchain-based… More >

  • Open Access

    ARTICLE

    Federated Feature Concatenate Method for Heterogeneous Computing in Federated Learning

    Wu-Chun Chung1, Yung-Chin Chang1, Ching-Hsien Hsu2,3, Chih-Hung Chang4, Che-Lun Hung4,5,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 351-371, 2023, DOI:10.32604/cmc.2023.035720

    Abstract Federated learning is an emerging machine learning technique that enables clients to collaboratively train a deep learning model without uploading raw data to the aggregation server. Each client may be equipped with different computing resources for model training. The client equipped with a lower computing capability requires more time for model training, resulting in a prolonged training time in federated learning. Moreover, it may fail to train the entire model because of the out-of-memory issue. This study aims to tackle these problems and propose the federated feature concatenate (FedFC) method for federated learning considering heterogeneous… More >

  • Open Access

    ARTICLE

    Internet of Things Intrusion Detection System Based on Convolutional Neural Network

    Jie Yin1,2,3,*, Yuxuan Shi1, Wen Deng1, Chang Yin1, Tiannan Wang1, Yuchen Song1, Tianyao Li1, Yicheng Li1

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2119-2135, 2023, DOI:10.32604/cmc.2023.035077

    Abstract In recent years, the Internet of Things (IoT) technology has developed by leaps and bounds. However, the large and heterogeneous network structure of IoT brings high management costs. In particular, the low cost of IoT devices exposes them to more serious security concerns. First, a convolutional neural network intrusion detection system for IoT devices is proposed. After cleaning and preprocessing the NSL-KDD dataset, this paper uses feature engineering methods to select appropriate features. Then, based on the combination of DCNN and machine learning, this paper designs a cloud-based loss function, which adopts a regularization method… More >

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