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 - 31 March 2023

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 >

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 - 09 March 2023

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 >

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 - 06 February 2023

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 >

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 - 06 February 2023

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 >

Mao Yuxin, Wang Honglin^*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 863-878, 2023, DOI:10.32604/cmc.2023.034879 - 06 February 2023

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… More >

N. Sundareswaran^*, S. Sasirekha

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2129-2143, 2023, DOI:10.32604/iasc.2023.034381 - 05 January 2023

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… More >

Lihong Guo^*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5069-5085, 2023, DOI:10.32604/cmc.2023.034571 - 28 December 2022

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… More >

R. S. Aashmi^1,*, T. Jaya²

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1295-1308, 2023, DOI:10.32604/csse.2023.027216 - 03 November 2022

Abstract The integration of clusters, grids, clouds, edges and other computing platforms result in contemporary technology of jungle computing. This novel technique has the aptitude to tackle high performance computation systems and it manages the usage of all computing platforms at a time. Federated learning is a collaborative machine learning approach without centralized training data. The proposed system effectively detects the intrusion attack without human intervention and subsequently detects anomalous deviations in device communication behavior, potentially caused by malicious adversaries and it can emerge with new and unknown attacks. The main objective is to learn overall… More >

Yinggang Sun¹, Hongguo Zhang¹, Chao Ma^1,*, Hai Huang¹, Dongyang Zhan^2,3, Jiaxing Qu⁴

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4043-4058, 2023, DOI:10.32604/cmc.2023.031684 - 31 October 2022

Abstract The problem of data island hinders the application of big data in artificial intelligence model training, so researchers propose a federated learning framework. It enables model training without having to centralize all data in a central storage point. In the current horizontal federated learning scheme, each participant gets the final jointly trained model. No solution is proposed for scenarios where participants only provide training data in exchange for benefits, but do not care about the final jointly trained model. Therefore, this paper proposes a new boosted tree algorithm, called RPBT (the originator Rights Protected federated… More >

Jieren Cheng¹, Zhenhao Liu^1,*, Yiming Shi¹, Ping Luo^1,2, Victor S. Sheng³

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1923-1939, 2023, DOI:10.32604/cmc.2023.032758 - 22 September 2022

Abstract With the increasing number of smart devices and the development of machine learning technology, the value of users’ personal data is becoming more and more important. Based on the premise of protecting users’ personal privacy data, federated learning (FL) uses data stored on edge devices to realize training tasks by contributing training model parameters without revealing the original data. However, since FL can still leak the user's original data by exchanging gradient information. The existing privacy protection strategy will increase the uplink time due to encryption measures. It is a huge challenge in terms of… More >