Yonghao Zhang^1,3, Yongtang Wu², Tao Li¹, Hui Zhou^1,3, Yuling Chen^1,2,*

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 third party for key distribution… More >

Ali Akbar Siddique¹, S. M. Umar Talha¹, M. Aamir¹, Abeer D. Algarni², Naglaa F. Soliman^2,*, Walid El-Shafai^3,4

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 preferred in most cases, and… More >

Zerui Zhen¹, Zihao Wu², Lei Feng^1,*, 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 resource consumption caused by frequent… More >

Yiqun Zhu¹, Shuxian Sun¹, Chunyu Liu¹, Xinyi Tian¹, Jingyi He², Shuai Xiao^2,*

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 federated learning method is proposed… More >

Wu-Chun Chung¹, Yung-Chin Chang¹, Ching-Hsien Hsu^2,3, Chih-Hung Chang⁴, Che-Lun Hung^4,5,*

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 clients. FedFC leverages the model… More >

Jie Yin^1,2,3,*, 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 to prevent overfitting. The model… More >

Mao Yuxin, Wang Honglin^*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 863-878, 2023, DOI:10.32604/cmc.2023.034879

Abstract In the financial sector, data are highly confidential and sensitive, and ensuring data privacy is critical. Sample fusion is the basis of horizontal federation learning, but it is suitable only for scenarios where customers have the same format but different targets, namely for scenarios with strong feature overlapping and weak user overlapping. To solve this limitation, this paper proposes a federated learning-based model with local data sharing and differential privacy. The indexing mechanism of differential privacy is used to obtain different degrees of privacy budgets, which are applied to the gradient according to the contribution degree to ensure privacy without… More >

N. Sundareswaran^*, S. Sasirekha

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2129-2143, 2023, DOI:10.32604/iasc.2023.034381

Abstract Smart internet of things (IoT) devices are used to manage domestic and industrial energy needs using sustainable and renewable energy sources. Due to cyber infiltration and a lack of transparency, the traditional transaction process is inefficient, unsafe and expensive. Smart grid systems are now efficient, safe and transparent owing to the development of blockchain (BC) technology and its smart contract (SC) solution. In this study, federated learning extreme gradient boosting (FL-XGB) framework has been developed along with BC to learn the intrusion inside the smart energy system. FL is best suited for a decentralized BC-enabled system to adapt learning models… More >

Lihong Guo^*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5069-5085, 2023, DOI:10.32604/cmc.2023.034571

Abstract To realize data sharing, and to fully use the data value, breaking the data island between institutions to realize data collaboration has become a new sharing mode. This paper proposed a distributed data security sharing scheme based on C/S communication mode, and constructed a federated learning architecture that uses differential privacy technology to protect training parameters. Clients do not need to share local data, and they only need to upload the trained model parameters to achieve data sharing. In the process of training, a distributed parameter update mechanism is introduced. The server is mainly responsible for issuing training commands and… More >

Weimin He, Lei Zhao^*

Journal of New Media, Vol.4, No.4, pp. 191-203, 2022, DOI:10.32604/jnm.2022.032994

Abstract Accurate electricity forecasting is the key basis for guiding the power sector to arrange operation plans and guaranteeing the profitability of electric power companies. However, with the increasing demand of enterprises and departments for data security, the phenomenon of “Isolated Data Island” becomes more and more serious, resulting in the accuracy loss of the traditional electricity prediction model. Federated learning, as an emerging artificial intelligence technology, is designed to ensure data privacy while carrying out efficient machine learning, which provides a new way to solve the problem of “Isolated Data Island” in terms of electricity forecasting. Nonetheless, due to the… More >