An efficient parallel machine learning-based blockchain framework

被引:14
作者
Tsai, Chun-Wei [1 ]
Chen, Yi-Ping [1 ]
Tang, Tzu-Chieh [1 ]
Luo, Yu-Chen [1 ]
机构
[1] Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung 80424, Taiwan
来源
ICT EXPRESS | 2021年 / 7卷 / 03期
关键词
Machine learning; Blockchain; Deep learning; OPTIMIZATION;
D O I
10.1016/j.icte.2021.08.014
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The unlimited possibilities of machine learning have been shown in several successful reports and applications. However, how to make sure that the searched results of a machine learning system are not tampered by anyone and how to prevent the other users in the same network environment from easily getting our private data are two critical research issues when we immerse into powerful machine learning-based systems or applications. This situation is just like other modern information systems that confront security and privacy issues. The development of blockchain provides us an alternative way to address these two issues. That is why some recent studies have attempted to develop machine learning systems with blockchain technologies or to apply machine learning methods to blockchain systems. To show what the combination of blockchain and machine learning is capable of doing, in this paper, we proposed a parallel framework to find out suitable hyperparameters of deep learning in a blockchain environment by using a metaheuristic algorithm. The proposed framework also takes into account the issue of communication cost, by limiting the number of information exchanges between miners and blockchain. (C) 2021 The Korean Institute of Communications and Information Sciences (KICS). Publishing services by Elsevier B.V.
引用
收藏
页码:300 / 307
页数:8
相关论文
共 50 条
[11]   Machine Learning-based Adaptive Access Control Mechanism for Private Blockchain Storage [J].
Almansoori, Sultan ;
Alzaabi, Mohamed ;
Alrayssi, Mohammed ;
Puthal, Deepak ;
Dutta, Joy ;
Al Shehhi, Aamna .
2023 IEEE 47TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE, COMPSAC, 2023, :1243-1248
[12]   ParSecureML: An Efficient Parallel Secure Machine Learning Framework on GPUs [J].
Chen, Zheng ;
Zhang, Feng ;
Zhou, Amelie Chi ;
Zhai, Jidong ;
Zhang, Chenyang ;
Du, Xiaoyong .
PROCEEDINGS OF THE 49TH INTERNATIONAL CONFERENCE ON PARALLEL PROCESSING, ICPP 2020, 2020,
[13]   Blockchain and Machine Learning-Based Hybrid IDS to Protect Smart Networks and Preserve Privacy [J].
Mishra, Shailendra .
ELECTRONICS, 2023, 12 (16)
[14]   A machine learning-based decision support framework for energy storage selection [J].
Li, Lanyu ;
Zhou, Tianxun ;
Li, Jiali ;
Wang, Xiaonan .
CHEMICAL ENGINEERING RESEARCH & DESIGN, 2022, 181 :412-422
[15]   A Machine Learning-Based Framework for the Prediction of Cervical Cancer Risk in Women [J].
Kaushik, Keshav ;
Bhardwaj, Akashdeep ;
Bharany, Salil ;
Alsharabi, Naif ;
Rehman, Ateeq Ur ;
Eldin, Elsayed Tag ;
Ghamry, Nivin A. .
SUSTAINABILITY, 2022, 14 (19)
[16]   A machine learning-based framework for cost-optimal building retrofit [J].
Deb, Chirag ;
Dai, Zhonghao ;
Schlueter, Arno .
APPLIED ENERGY, 2021, 294
[17]   A machine learning-based process operability framework using Gaussian processes [J].
Alves, Victor ;
Gazzaneo, Vitor ;
Lima, Fernando, V .
COMPUTERS & CHEMICAL ENGINEERING, 2022, 163
[18]   Framework for Testing Robustness of Machine Learning-Based Classifiers [J].
Chuah, Joshua ;
Kruger, Uwe ;
Wang, Ge ;
Yan, Pingkun ;
Hahn, Juergen .
JOURNAL OF PERSONALIZED MEDICINE, 2022, 12 (08)
[19]   Optimizing diabetes classification with a machine learning-based framework [J].
Feng, Xin ;
Cai, Yihuai ;
Xin, Ruihao .
BMC BIOINFORMATICS, 2023, 24 (01)
[20]   Optimizing diabetes classification with a machine learning-based framework [J].
Xin Feng ;
Yihuai Cai ;
Ruihao Xin .
BMC Bioinformatics, 24