基于深度学习的联邦集成算法

doi:10.3969/j.issn.0255-8297.2022.03.012

Abstract

Abstract: Federated learning is a research hotspot in the field of multi-source privacy data protection. It has advantage that its framework can train a common model that is satisfactory to many parties when the data is not local, but it hardly integrates local model parameters and cannot make full use of multiple sources under safe conditions. Aiming to the problem, this article proposes a deep learning-based federated integration algorithm,which applies deep learning and integrated learning to the framework of federated learning. Under the framework of the proposed federated learning, the parameters of local modes are optimized, accordingly the accuracy of the local model is improved. Besides, by applying variety of integration algorithms to integrate local model parameters, the improvement of model accuracy with taking into account the security of multi-source data simultaneously can be achieved. Experimental comparisons with traditional multi-source data processing technology are demonstrated, and show that the accuracies of the proposed training model on the mnist, digits, letter, and wine data sets are increased by 1%, 8%, -1%, 1%, respectively. So that the proposed algorithm could guarantee the improvement of accuracy and the security of multi-source data and models at the same time, and this has important practical application value.

Key words: federated learning, federated ensemble algorithm, deep neural network (DNN) model, ensemble algorithm, deep learning

CLC Number:

TP391

LUO Changyin, CHEN Xuebin, SONG Shangwen, ZHANG Shufen, LIU Zhiyu. Federated Ensemble Algorithm Based on Deep Neural Network[J]. Journal of Applied Sciences, 2022, 40(3): 493-510.

References

[1] Mcmahan H B, Moore E, Ramage, et al. Communication-efficient learning of deep networks form decentralized data[C]//Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017:1-11.
[2] Konecny J, Mcmahan H B, Yu F X, et al. Federated learning:strategies for improving communication efficiency[C]//Proceedings of the 30th Annual Conference on Neural Information Processing Systems, 2016:1-10.
[3] Yang Q. Challenges of GDPR to AI and countermeasures based on federal transfer learning[J]. Communications of Chinese Association of Artificial Intelligence, 2018, 8:1-8.
[4] Yang Q, Liu Y, Chen T J, et al. Federated machine learning:concept and applications[J]. ACM Transactions on Intelligent Systems and Technology, 2019, 10(2):1-19.
[5] Wang S, Tuor T, Salonidis T, et al. Adaptive federated learning in resource constrained edge computing systems[J]. IEEE Journal on Selected Areas in Communications, 2019, 37(6):1205-1221.
[6] Liu Y, Liu Y, Liu Z, et al. Federated Forest[J/OL].[2020-06-23]. https://arxiv.org/pdf/1905.10053v1.pdf.
[7] Sharma S, Chen K. Poster:privacy-preserving boosting with random linear classifiers[C]//Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, 2018:1-13.
[8] Sun C, Shrivastava A. Revisiting unreasonable effectiveness of data in deep learning era[C]//2017 IEEE International Conference on Computer Vision (ICCV), 2017, 843-852.
[9] Kim H, Park J, Bennis M, et al. On-device federated learning via block chain and its latency analysis[DB/OL].[2020-06-23]. https://arxiv.org/pdf/1808.03949.pdf.
[10] Li S Y, Cheng Y, Liu Y, et al. Abnormal client behavior detection in federated learning[DB/OL].[2020-06-23]. https://arxiv.org/pdf/1910.09933.pdf.
[11] Zhun L G, Liu Z J, Liu Z J, et al. Deep Leakage from Gradients[DB/OL].[2020-06-23]. https://arxiv.org/pdf/1906.08935.
[12] 刘之瑜,张淑芬,刘洋,等.基于图像梯度的数据增广方法[J].应用科学学报, 2021, 39(2):302-311. Liu Z Y, Zhang S F, Liu Y, et al. Data augmentation method based on image gradient[J]. Journal of Applied Science, 2021, 39(2):302-311.(in Chinese)
[13] Gao H H, Huang W Q, Yang X X. Applying probabilistic model checking to path planning in an intelligent transportation system using mobility trajectories and their statistical data[J]. Intelligent Automation and Soft Computing, 2019, 25(3):547-559.
[14] Gao H H, Huang W Q, Duan Y C, et al. Research on cost-driven services composition in an uncertain environment[J]. Journal of Internet Technology (JIT), 2019, 20(3):755-769.
[15] Preuveneers D, Rimmer V, Tsingenopoulos I, et al. Chained anomaly detection models for federated learning:an intrusion detection case study[J]. Applied Sciences, 2018, 8(12):1-12.
[16] Brisimi T S, Chen R, Mela T, et al. Federated learning of predictive models from federated Electronic Health Records[J]. International Journal of Medical Informatics, 2018, 112:59-67.
[17] Zhang W S, Zhang Y J, Zhai J, et al. Multi-source data fusion using deep learning for smart refrigerators[J]. Computers in Industry, 2018, 95:15-21.
[18] Lee J, Wang F, Sun J M, et al. Privacy-preserving patient similarity learning in a federated environment:development and analysis[J]. JMIR Medical Informatics, 2018, 6(2):e20.
[19] Shen G J, Han X, Zhou J J, et al. Research on intelligent analysis and depth fusion of multi-source traffic data[J]. IEEE Access, 2018, 6:59329-59335.
[20] Liu J, Li T R, Xie P, et al. Urban big data fusion based on deep learning:an overview[J]. Information Fusion, 2020, 53:123-133.
[21] Rivest R, Shamir A, Adleman L. A method for obtaining digital signatures and public-key cryptosystems[J]. Communications of the ACM, 1978, 21(2):120-126.
[22] 娄悦,施荣华,曹龄兮.基于强认证技术的会话初始协议安全认证模型[J].计算机应用, 2006, 30(10):2332-2335. Lou Y, Shi R H, Cao L X. Security authenti cation model of session initiation protocol based on strong authentication technology[J]. Journal of Computer Applications, 2006, 30(10):2332-2335.(in Chinese)
[23] 鲁莹,郑少智. Stacking学习与一般集成方法的比较研究[J/OL].中国科技论文在线精品论文, 2018, 11(4):372-379. Lu Y, Zheng S Z. A comparative study of stacking learning and general integration methods[J/OL]. Highlights of Science paper Online, 2018, 11(4):372-379.(in Chinese)
[24] 陈玉昇,杨燕华,林萌,等.基于深度学习神经网络的核电厂故障诊断技术[J].上海交通大学学报, 2018, 52(S1):58-61. Chen Y S, Yang Y H, Lin M, et al. Nuclear power plant fault diagnosis technology based on deep learning neural network[J]. Journal of Shanghai JiaoTong University, 2018, 52(Suppl.1):58-61.(in Chinese)

Federated Ensemble Algorithm Based on Deep Neural Network

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	ZHU Li, YANG Qing, WU Tao, LI Chen, LI Ming. Emotional Analysis of Brain Waves Based on CNN and Bi-LSTM [J]. Journal of Applied Sciences, 2022, 40(1): 1-12.
[2]	LEI Qianhui, PAN Lili, SHAO Weizhi, HU Haipeng, HUANG Yao. Segmentation Model of COVID-19 Lesions Based on Triple Attention Mechanism [J]. Journal of Applied Sciences, 2022, 40(1): 105-115.
[3]	KANG Huixian, YI Biao, WU Hanzhou. Recent Advances in Text Steganography and Steganalysis [J]. Journal of Applied Sciences, 2021, 39(6): 923-938.
[4]	OU Qiaofeng, XIAO Jiabing, XIE Qunqun, XIONG Bangshu. Multi-target Detection and Recognition for Vehicle Inspection Images Based on Deep Learning [J]. Journal of Applied Sciences, 2021, 39(6): 939-951.
[5]	LI Wenju, HE Maoxian, ZHANG Yaoxing, CHEN Huiling, LI Peigang. Crack Detection of Track Slab Based on Convolutional Neural Network and Voting Mechanism [J]. Journal of Applied Sciences, 2021, 39(4): 627-640.
[6]	GUO Yubo, LU Jun, DUAN Pengqi. Automatic Classification of Bamboo Flute Playing Skills Based on Deep Learning [J]. Journal of Applied Sciences, 2021, 39(4): 685-694.
[7]	HAO Yan, SHI Huiyu, HUO Shoujun, HAN Dan, CAO Rui. Emotion Classification Based on EEG Deep Learning [J]. Journal of Applied Sciences, 2021, 39(3): 347-346.
[8]	DU Chengze, DUAN Youxiang, SUN Qifeng. Seismic Fault Identification Method Based on ResUNet and Dense CRF Model [J]. Journal of Applied Sciences, 2021, 39(3): 367-366.
[9]	WANG Wanguo, MU Shiyou, LIU Yue, LIU Guangxiu, LANG Fenling. Research on Insulator Self Exploding Detection in UAV Inspection Based on Deep Learning [J]. Journal of Applied Sciences, 2021, 39(2): 222-231.
[10]	LIU Zhiyu, ZHANG Shufen, LIU Yang, LUO Changyin, LI Min. Data Augmentation Method Based on Image Gradient [J]. Journal of Applied Sciences, 2021, 39(2): 302-311.
[11]	LEI Kai, HUANG Shuokang, FANG Junjie, HUANG Jiyue, XIE Yingying, PENG Bo. Intelligent Eco Networking (IEN): Knowledge-Driven and Value-Oriented Future Internet Infrastructure [J]. Journal of Applied Sciences, 2020, 38(1): 152-172.
[12]	WANG Canjun, LIAO Xin, CHEN Jiaxin, QIN Zheng, LIU Xuchong. Research on Facial Modification Detection Algorithm Based on Convolutional Neural Network [J]. Journal of Applied Sciences, 2019, 37(5): 618-630.
[13]	ZHU Yiming, CHEN Fan, HE Hongjie, CHEN Hongyou. Orthogonal GAN Information Hiding Model Based on Secret Information Driven [J]. Journal of Applied Sciences, 2019, 37(5): 721-732.
[14]	LI Fang, ZHONG Yingchun, QI Jian, LUO Peng. Automatic Extraction of Regions of Peripheral Nerve Internal Fascicular Groups from MicroCT Images [J]. Journal of Applied Sciences, 2019, 37(3): 359-368.
[15]	YANG Bin, ZHANG Tao, CHEN Xian-yi. Local Blur Detection of Digital Images Based on Deep Learning [J]. Journal of Applied Sciences, 2018, 36(2): 321-330.