Survey on vertical federated learning: algorithm, privacy and security

Jinyin CHEN; Rongchang LI; Guohan HUANG; Tao LIU; Haibin ZHENG; Yao CHENG

doi:10.11959/j.issn.2096-109x.2023017

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Survey on vertical federated learning: algorithm, privacy and security

Comprehensive Reviews | 更新时间：2024-06-03

- Survey on vertical federated learning: algorithm, privacy and security
- Chinese Journal of Network and Information Security Vol. 9, Issue 2, Pages: 1-20(2023)
- 作者机构：
  
  1. 浙江工业大学网络空间安全研究院，浙江杭州 310023
  2. 浙江工业大学信息工程学院，浙江杭州 310023
  3. 南德认证检测亚太有限公司，新加坡 60993
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2023017
  CLC： TP18
- Online First：2023-04，
  
  Published：25 April 2023
- 稿件说明：
移动端阅览
Jinyin CHEN, Rongchang LI, Guohan HUANG, et al. Survey on vertical federated learning: algorithm, privacy and security[J]. Chinese Journal of Network and Information Security, 2023, 9(2): 1-20.
DOI：

Jinyin CHEN, Rongchang LI, Guohan HUANG, et al. Survey on vertical federated learning: algorithm, privacy and security[J]. Chinese Journal of Network and Information Security, 2023, 9(2): 1-20. DOI： 10.11959/j.issn.2096-109x.2023017.

摘要

联邦学习（FL，federated learning）是一种新兴的分布式机器学习技术，利用分散在各个机构的数据，通过传输中间结果（如模型参数、参数梯度、嵌入信息等）实现机器学习模型的联合构建。联邦学习中机构的训练数据不允许离开本地，因此降低了数据泄露的风险。根据机构之间数据分布的差异，FL 通常分为横向联邦学习（HFL，horizontal FL）、纵向联邦学习（VFL，vertical FL），以及联邦迁移学习（TFL， transfer FL）。其中，VFL适用于机构具有相同样本空间但不同特征空间的场景，广泛应用于医疗诊断、金融评估和教育服务等领域。尽管 VFL 在现实应用中有出色的表现，但其本身仍然面临诸多隐私和安全问题，尚缺少对VFL方法与安全性展开全面综述的工作。为了构建高效且安全的VFL系统，从VFL方法及其隐私和安全两个方面展开，首先从边缘模型、通信机制、对齐机制以及标签处理机制4个角度对现有的VFL方法进行详细总结和归纳；其次介绍并分析了 VFL 面临的隐私和安全风险；进一步对其防御方法进行介绍和总结；此外，介绍了适用于VFL的常见数据集及平台框架。结合VFL面临的安全性挑战给出了VFL的未来研究方向，旨在为构建高效、鲁棒和安全的VFL的理论研究提供参考。

Abstract

Federated learning （FL） is a distributed machine learning technology that enables joint construction of machine learning models by transmitting intermediate results （e.g.

model parameters

parameter gradients

embedding representation

etc.） applied to data distributed across various institutions.FL reduces the risk of privacy leakage

since raw data is not allowed to leave the institution.According to the difference in data distribution between institutions

FL is usually divided into horizontal federated learning （HFL）

vertical federated learning （VFL）

and federal transfer learning （TFL）.VFL is suitable for scenarios where institutions have the same sample space but different feature spaces and is widely used in fields such as medical diagnosis

financial and security of VFL.Although VFL performs well in real-world applications

it still faces many privacy and security challenges.To the best of our knowledge

no comprehensive survey has been conducted on privacy and security methods.The existing VFL was analyzed from four perspectives: the basic framework

communication mechanism

alignment mechanism

and label processing mechanism.Then the privacy and security risks faced by VFL and the related defense methods were introduced and analyzed.Additionally

the common data sets and indicators suitable for VFL and platform framework were presented.Considering the existing challenges and problems

the future direction and development trend of VFL were outlined

to provide a reference for the theoretical research of building an efficient

robust and safe VFL.

关键词

Keywords

references

焦李成 , 杨淑媛 , 刘芳 , 等 . 神经网络七十年:回顾与展望 [J ] . 计算机学报 , 2016 , 39 ( 8 ): 1697 - 1716 .

JIAO L C , YANG S Y , LIU F , et al . Seventy years beyond neural networks：retrospect and prospect [J ] . Chinese Journal of Computers , 2016 , 39 ( 8 ): 1697 - 1716 .

中华人民共和国数据安全法 [N ] . 中华人民共和国全国人民代表大会常务委员会公报 , 2021 ( 5 ): 951 - 956 .

FData Security Law of the People＇s Republic of China [N ] . Communiqué of the Standing Committee of the National People＇s Congress of the People＇s Republic of China , 2021 ( 5 ): 951 - 956 .

GODDARD M . The EU general data protection regulation （GDPR）:European regulation that has a global impact [J ] . International Journal of Market Research , 2017 , 59 ( 6 ): 703 - 705 .

HORD A . Federated learning for mobile keyboard prediction [J ] . arXiv preprint arXiv:1811.03604 , 2018 .

YANG Q , LIU Y , CHEN T , et al . Federated machine learning:concept and applications [J ] . ACM Transactions on Intelligent Systems and Technology （TIST） , 2019 , 10 ( 2 ): 1 - 19 .

李少波 , 杨磊 , 李传江 , 等 . 联邦学习概述：技术、应用及未来 [J ] . 计算机集成制造系统 , 2021 , 1 ( 10 ): 1 - 29 .

LI S B , YANG L , LI C J , et al . Overview of federated learning:technology,applications and future [J ] . Computer Integrated Manufacturing Systems , 2021 , 1 ( 10 ): 1 - 29 .

周传鑫 , 孙奕 , 汪德刚 , 等 . 联邦学习研究综述 [J ] . 网络与信息安全学报 , 2021 , 7 ( 5 ): 1 - 16 .

ZHOU C X , SUN Y , WANG D , et al . Survey of federated learning research [J ] . Chinese Journal of Network and Information Security , 2021 , 7 ( 5 ): 1 - 16 .

卫新乐 , 张志勇 , 宋斌 , 等 . 基于纵向联邦学习的社交网络跨平台恶意用户检测方法 [J ] . 小型微型计算机系统 , 2021 ,( 10 ): 1 - 9 .

WEI X L , ZHANG Z Y , SONG B , et al . Social networks cross-platform malicious user detection method based on vertical federated learning [J ] . Journal of Chinese Mini-Micro Computer Systems , 2021 ,( 10 ): 1 - 9 .

李鸣 . 基于纵向联邦学习的推荐系统技术研究 [D ] . 杭州:浙江大学 , 2021 .

LI M . Research on recommendation system technology based on vertical federated learning [D ] . Hangzhou:Zhejiang University , 2021 .

KANG Y , LIU Y , CHEN T J . Fedmv:semi-supervised vertical federated learning with multiview training [J ] . arXiv preprint arXiv:2008.10838 , 2020 .

WU Z , LI Q , HE B . Exploiting record similarity for practical vertical federated learning [J ] . arXiv preprint arXiv:2106.06312 , 2021 .

LUO X , WU Y , XIAO X , et al . Feature inference attack on model predictions in vertical federated learning [C ] // Proceedings of IEEE 37th International Conference on Data Engineering (ICDE) . 2021 : 181 - 192 .

LI O , SUN J , YANG X , et al . Label leakage and protection in two-party split learning [J ] . arXiv preprint arXiv:2102.08504 , 2021 .

WENG H , ZHANG J , XUE F , et al . Privacy leakage of real-world vertical federated learning [J ] . arXiv preprint arXiv:2011.09290 , 2020 .

LIU Y , YI Z , CHEN T . Backdoor attacks and defenses in feature-partitioned collaborative learning [J ] . arXiv preprint arXiv:2007.03608 , 2020 .

HARDY S , HENECKA W , IVEY-LAW H , , et al . Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption [J ] . arXiv preprint arXiv:1711.10677 , 2017 .

YANG K , FAN T , CHEN T , et al . A quasi-Newton method based vertical federated learning framework for logistic regression [J ] . arXiv preprint arXiv:1912.00513 , 2019 .

YANG S , REN B , ZHOU X , et al . Multi-VFL for vertical federated learning without third-party coordinator [J ] . arXiv preprint arXiv:1911.09824 , 2019 .

CHENG K , FAN T , JIN Y , et al . SecureBoost:a lossless federated learning framework [J ] . IEEE Intelligent Systems , 2021 , 36 ( 6 ): 87 - 98 .

WU Y , CAI S , XIAO X , et al . Privacy preserving vertical federated learning for tree-based models [C ] // Proceedings of the VLDB Endowment , 2020 . 2090 - 2103 .

VEPAKOMMA P , GUPTA O , SWEDISH T , et al . Split learning for health:distributed deep learning without sharing raw patient data [J ] . arXiv preprint arXiv:1812.00564 , 2018 .

FU C , ZHANG X , JI S , et al . Label inference attacks against vertical federated learning [C ] // Proceedings of USENIX Security Symposium . 2022 : 1397 - 1414 .

NI X , XU X , LYU L , et al . A vertical federated learning framework for graph convolutional network [J ] . arXiv preprint arXiv:2106.11593 , 2021 .

ZHOU J , CHEN C , ZHENG L , et al . Vertically federated graph neural network for privacy-preserving node classification [J ] . arXiv preprint arXiv:2005.11903 , 2020 .

GU B , XU A , HUO Z , et al . Privacy-preserving asynchronous federated learning algorithms for multi-party vertically collaborative learning [J ] . arXiv preprint arXiv:2008.06233 , 2020 .

MCMAHAN H B , MOORE E , RAMAGE D , et al . Federated learning of deep networks using model averaging [J ] . arXiv preprint arXiv:1602.05629 , 2016 .

LIU Y , KANG Y , ZHANG X , et al . A communication efficient collaborative learning framework for distributed features [J ] . arXiv preprint arXiv:1912.11187 , 2019 .

CHEN T , JIN X , SUN Y , et al . VAFL:a method of vertical asynchronous federated learning [J ] . arXiv preprint arXiv:2007.06081 , 2020 .

DAS A , PATTERSON S . Multi-tier federated learning for vertically partitioned data [C ] // Proceedings of IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP) . 2021 : 3100 - 3104 .

WRIGHT S , NOCEDAL J . Numerical optimization [J ] . Springer Science , 1999 , 35 ( 67-68 ): 7 .

ZHANG Q , GU B , DENG C , et al . AsySQN:faster vertical federated learning algorithms with better computation resource utilization [C ] // Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery ＆ Data Mining . 2021 : 3917 - 3927 .

LIN Y J . Deep gradient compression:reducing the communication bandwidth for distributed training [J ] . arXiv preprint arXiv:1712.01887 , 2017 .

YANG K , SONG Z , ZHANG Y , et al . Model optimization method based on vertical federated learning [C ] // Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS) . 2021 : 1 - 5 .

LI M , CHEN Y , WANG Y , et al . Efficient asynchronous vertical federated learning via gradient prediction and double-end sparse compression [C ] // Proceedings of 16th International Conference on Control,Automation,Robotics and Vision (ICARCV) . 2020 : 291 - 296 .

LIU D , . Accelerating intra-party communication in vertical federated learning with RDMA [C ] // Proceedings of the 1st Workshop on Distributed Machine Learning . 2020 : 14 - 20 .

黄翠婷 , 张帆 , 孙小超 , 等 . 隐私集合求交技术的理论与金融实践综述 [J ] . 信息通信技术与政策 , 2021 , 47 ( 6 ): 50 .

HUANG C T , ZHANG F , SUN X C , et al . A survey of private set intersection technology and finance practice [J ] . Information and Communications Technology and Policy , 2021 , 47 ( 6 ): 50 .

MEADOWS C , . A more efficient cryptographic matchmaking protocol for use in the absence of a continuously available third party [C ] // Proceedings of 1986 IEEE Symposium on Security and Privacy . 1986 : 134 - 134 .

DE CRISTOFARO E , TSUDIK G . Experimenting with fast private set intersection [C ] // Proceedings of International Conference on Trust and Trustworthy Computing . 2012 : 55 - 73 .

RABIN M O . How To Exchange Secrets with Oblivious Transfer [J ] . IACR Cryptol , 2005 , 2005 ( 187 ): 1 - 26 .

ISHAI Y , KILIAN J , NISSIM K , et al . Extending oblivious transfers efficiently [C ] // Proceedings of Annual International Cryptology Conference . 2003 : 145 - 161 .

KISSNER L , SONG D . Privacy-preserving set operations [C ] // Proceedings of Annual International Cryptology Conference . 2005 : 241 - 257 .

CAMENISCH J , ZAVERUCHA G M . Private intersection of certified sets [C ] // Proceedings of International Conference on Financial Cryptography and Data Security . 2009 : 108 - 127 .

ZHANG Q , GU B , DENG C , et al . Secure bilevel asynchronous vertical federated learning with backward updating [J ] . arXiv preprint arXiv:2103.00958 , 2021 .

XIA W , LI Y , ZHANG L , et al . A vertical federated learning framework for horizontally partitioned labels [J ] . arXiv preprint arXiv:2106.10056 , 2021 .

MUGUNTHAN V , GOYAL P , KAGAL L . Multi-VFL:a vertical federated learning system for multiple data and label owners [J ] . arXiv preprint arXiv:2106.05468 , 2021 .

REDDI S , CHARLES Z , ZAHEER M , et al . Adaptive federated optimization [J ] . arXiv preprint arXiv:2003.00295 , 2020 .

FENG S , YU H . Multi-participant multi-class vertical federated learning [J ] . arXiv preprint arXiv:2001.11154 , 2020 .

HU Y , LIU P , KONG L , et al . Learning privately over distributed features:An ADMM sharing approach [J ] . arXiv preprint arXiv:1907.07735 , 2019 .

GU B , DANG Z , LI X , et al . Federated doubly stochastic kernel learning for vertically partitioned data [C ] // Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery＆ Data Mining . 2020 : 2483 - 2493 .

CHEN X , LI J , CHAKRABARTI C . Communication and computation reduction for split learning using asynchronous training [J ] . arXiv preprint arXiv:2107.09786 , 2021 .

ZHANG S , XIANG L , YU X , et al . Privacy-preserving federated learning on partitioned attributes [J ] . arXiv preprint arXiv:2104.14383 , 2021 .

SHOKRI R , STRONATI M , SONG C , et al . Membership inference attacks against machine learning models [C ] // Proceedings of 2017 IEEE Symposium on Security and Privacy (SP) . 2017 : 3 - 18 .

LIU Y , ZHANG X , WANg L . Asymmetrical vertical federated learning [J ] . arXiv preprint arXiv:2004.07427 , 2020 .

ZHU L , LIU Z , HAN S . Deep leakage from gradients [J ] . Advances in Neural Information Processing Systems , 2019 , 32 : 14774 - 14784 .

GEIPING J , BAUERMEISTER H , DRÖGE H , , et al . Inverting gradients--how easy is it to break privacy in federated learning [J ] . arXiv preprint arXiv:2003.14053 , 2020 .

YIN H , MALLYA A , VAHDAT A , et al . See through gradients:image batch recovery via grad inversion [C ] // Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 2021 : 16337 - 16346 .

MAHENDRAN A , VEDALDI A . Understanding deep image representations by inverting them [C ] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2015 : 5188 - 5196 .

PASQUINI D , ATENIESE G , BERNASCHI M . Unleashing the tiger:inference attacks on split learning [J ] . arXiv preprint arXiv:2012.02670 , 2020 .

GU T , LIU K , DOLAN-GAVITT B , , et al . Badnets:evaluating backdooring attacks on deep neural networks [J ] . IEEE Access , 2019 , 7 : 47230 - 47244 .

LIAO C , ZHONG H , SQUICCIARINI A , et al . Backdoor embedding in convolutional neural network models via invisible perturbation [J ] . arXiv preprint arXiv:1808.10307 , 2018 .

XIE C , HUANG K , CHEN P Y , et al . Dba:distributed backdoor attacks against federated learning [C ] // Proceedings of International Conference on Learning Representations . 2019 : 1 - 19 .

SHAFAHI A , HUANG W R , NAJIBI M , et al . Poison frogs! targeted clean-label poisoning attacks on neural networks [J ] . arXiv preprint arXiv:1804.00792 , 2018 .

TURNER A , TSIPRAS D , MADRY A . Label-consistent backdoor attacks [J ] . arXiv preprint arXiv:1912.02771 , 2019 .

WENG C H , LEE Y T , WU S H B . On the trade-off between adversarial and backdoor robustness [J ] . Advances in Neural Information Processing Systems , 2020 ,33.

GOODFELLOW I J , SHLENS J , SZEGEDY C . Explaining and harnessing adversarial examples [J ] . arXiv preprint arXiv:1412.6572 , 2014 .

LIU J , XIE C , KENTHAPADI K , et al . Rvfr:Robust vertical federated learning via feature subspace recovery [C ] // Proceedings of NeurIPS Workshop New Frontiers in Federated Learning:Privacy,Fairness,Robustness,Personalization and Data Ownership . 2021 : 1 - 9 .

CHEN J Y . Graph-fraudster:adversarial attacks on graph neural network based vertical federated learning [J ] . arXiv preprint arXiv:2110.06468 , 2021 .

RONALD L R , ADLEMAN L , DERTOUZOS M L . On data banks and privacy homomorphisms [J ] . Foundations of Secure Computation , 1978 , 4 ( 11 ): 169 - 180 .

GENTRY C , . Fully homomorphic encryption using ideal lattices [C ] // Proceedings of the forty-first annual ACM symposium on Theory of computing . 2009 : 1 - 10 .

OU W . A homomorphic-encryption-based vertical federated learning scheme for rick management [J ] . Computer Science and Information Systems , 2020 :22.

夏家骏 , 鲁颖 , 张子扬 , 等 . 基于秘密共享与同态加密的纵向联邦学习方案研究 [J ] . 信息通信技术与政策 , 2021 , 47 ( 6 ): 19 - 26 .

XIA J J , LU Y , ZHANG Z Y , et al . Research on vertical federated learning based on secret sharing and homomorphic encryption [J ] . Information and Communications Technology and Policy , 2021 , 47 ( 6 ): 19 - 26 .

DWORK C , MCSHERRY F , NISSIM K , et al . Calibrating noise to sensitivity in private data analysis [C ] // Proceedings of Theory of Cryptography Conference . 2006 : 265 - 284 .

ABADI M , CHU A , GOODFELLOW I , et al . Deep learning with differential privacy [C ] // Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security . 2016 : 308 - 318 .

WANG C , LIANG J , HUANG M , et al . Hybrid differentially private federated learning on vertically partitioned data [J ] . arXiv preprint arXiv:2009.02763 , 2020 .

SZEGEDY C , ZAREMBA W , SUTSKEVER I , et al . Intriguing properties of neural networks [J ] . Computer Science , 2013 .

ELAZAR Y , GOLDBERG Y . Adversarial removal of demographic attributes from text data [J ] . arXiv preprint arXiv:1808.06640 , 2018 .

LIAO P , ZHAO H , XU K , et al . Information obfuscation of graph neural networks [C ] // Proceedings of International Conference on Machine Learning . 2021 : 6600 - 6610 .

ZHANG S , XIANG L , YU X , et al . Privacy-preserving federated learning on partitioned attributes [J ] . arXiv preprint arXiv:2104.14383 , 2021 .

SUN J , YAO Y , GAO W , et al . Defending against reconstruction attack in vertical federated learning [J ] . arXiv preprint arXiv:2107.09898 , 2021 .

LIU Y , KANG Y , ZOU T , et al . Vertical Federated Learning [J ] . arXiv preprint arXiv:2211.12814 , 2022 .

JOHNSON A E W , POLLARD T J , SHEN L , et al . MIMIC-III,a freely accessible critical care database [J ] . Scientific data , 2016 , 3 ( 1 ): 1 - 9 .

STREET W N , WOLBERG W H , MANGARIAN O L . Nuclear feature extraction for breast tumor diagnosis [J ] . In Biomedical image processing and biomedical visualization , 1993 , 1905 : 861 - 870 .

MORO S , CORTEZ P , RITA P . A data-driven approach to predict the success of bank telemarketing [J ] . Decision Support Systems , 2014 , 62 : 22 - 31 .

YEH I , LIEN C . The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients [J ] . Expert systems with applications , 2009 , 36 ( 2 ): 2473 - 2480 .

KOHAVI R . Scaling up the accuracy of naive-bayes classifiers:A decision-tree hybrid [J ] . Kdd , 1996 , 96 : 202 - 207 .

ZHAO P , XIAO K , ZHANG Y , et al . AMEIR:Automatic Behavior Modeling,Interaction Exploration and MLP Investigation in the Recommender System [C ] // Proceedings of International Joint Conferences on Artifi-cial Intelligence . 2021 : 2104 - 2110 .

MCCALLUM A K , NIGAM K , RENNIE J , et al . Automating the construction of internet portals with machine learning [J ] . Information Retrieval , 2000 , 3 : 127 - 163 .

SEN P , NAMATA G , BILGIC M , et al . Collective classification in network data [J ] . AI magazine , 2008 , 29 ( 3 ): 93 .

DENG L . The MNIST database of handwritten digit images for machine learning research [J ] . IEEE signal processing magazine , 2021 , 29 ( 6 ): 141 - 142 .

XIAO H , RASUL K , VOLLGRAF R . Fashion-MNIST:a novel image dataset for benchmarking machine learning algorithms [J ] . arXiv preprint arXiv:1708.07747 , 2017 .

KRIZHEVSKY A , HINTON G . Learning multiple layers of features from tiny images [R ] . Technical report,Citeseer , 2009 .

REN M , KIROS R , ZEMEL R . Exploring models and data for image question answering [J ] . Advances in neural information processing systems , 2015 , 28 : 2953 - 2961 .

CHUA T S , TANG J , HONG R , et al . NUS-WIDE:a real-world web image database from national university of Singapore [C ] // Proceedings of ACM International Conference on Image and Video Retrieval . 2009 : 1 - 9 .

LIU Z , LUO P , WANG X , et al . Deep learning face attributes in the wild [C ] // Proceedings of IEEE International Conference on Computer Vision . 2015 : 3730 - 3738 .

FERNANDES K , VINAGRE P , CORTEZ P . A proactive intelligent decision support system for predicting the popularity of online news [C ] // Proceedings of Portuguese Conference on Artificial Intelligence . 2015 : 535 - 546 .

ZHANG X , ZHAO J , LECUN Y . Character-level convolutional networks for text classification [C ] // Proceedings of The Advances in neural information processing systems . 2015 : 1 - 9 .

HAMIDIEH K . A data-driven statistical model for predicting the critical temperature of a superconductor [J ] . Computational Materials Science , 2018 , 154 : 346 - 354 .

CANDANEDO L M , FELDHEIM V , DERAMAIX D . Data driven prediction models of energy use of appliances in a low-energy house [J ] . Energy and buildings , 2017 , 140 : 81 - 97 .

ZHANG C , ZHANG J , CHAI D , et al . Aegis:A trusted,automatic and accurate verification framework for vertical federated learning [J ] . arXiv preprint arXiv:2108.06958 , 2021 .

ZHU H , WANG R , JIN Y , et al . PIVODL:privacy-preserving vertical federated learning over distributed labels [J ] . arXiv preprint arXiv:2108.11444 , 2021 .

WENJIE S , XUAN S . Vertical federated learning based on DFP and BFGS [J ] . arXiv preprint arXiv:2101.09428 , 2021 .

SUN J , YANG X , YAO Y , et al . Vertical federated learning without revealing intersection membership [J ] . arXiv preprint arXiv:2106.05508 , 2021 .

SHAN C , JIAO H , FU J . Towards representation identical privacy-preserving graph neural network via split learning [J ] . arXiv preprint arXiv:2107.05917 , 2021 .

XIA W , LI Y , ZHANG L , et al . A Vertical federated learning framework for horizontally partitioned labels [J ] . arXiv preprint arXiv:2106.10056 , 2021 .

ROMANINI D , HALL A J , Papadopoulos P , et al . Pyvertical:a vertical federated learning framework for multi-headed splitnn [J ] . arXiv preprint arXiv:2104.00489 , 2021 .

GAO Y , DOAN B G , ZHANG Z , et al . Backdoor attacks and countermeasures on deep learning:a comprehensive review [J ] . arXiv preprint arXiv:2007.10760 , 2020 .

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