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NTIS 바로가기Journal of physics. Conference series, v.1757 no.1, 2021년, pp.012192 -
Mao, Juan , Cao, Chunjie , Wang, LongJuan , Ye, Jun , Zhong, WenJie
AbstractWith the emergence of data islands and the popular awareness of privacy, federated learning, as an emerging data sharing and exchange model, can realize multi-party collaboration under the premise of protecting data privacy and security because the data distributed in multiple devices cannot...
Research on Information Security Yang 5 961 2019 AI and data privacy protection: the solution to Federated learning
ACM Transactions on Intelligent Systems and Technology (TIST) Yang 10 1 2019 10.1145/3339474 Federated machine learning: Concept and applications
IEEE transactions on knowledge and data engineering Lu 8 957 1996 10.1109/69.553163 Effective data mining using neural networks
Ateniese 2013 Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers J
Wu 2016 A Methodology for Formalizing Model-Inversion Attacks 2016 IEEE 29th Computer Security Foundations Symposium (CSF) IEEE
IEEE Transactions on Information Forensics and Security Xu 15 911 2019 10.1109/TIFS.2019.2929409 Verifynet: Secure and verifiable federated learning
Zhang 374 2019 August Poisoning attack in federated learning using generative adversarial nets In
Du 222 2004 April Privacy-preserving multivariate statistical analysis: Linear regression and classification In
Bogdanov 192 2008
Hardy 2017 Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
IEEE Transactions on Information Forensics and Security Aono 13 1333 2017 Privacy-preserving deep learning via additively homomorphic encryption
Choudhury 2019 Differential privacy-enabled federated learning for sensitive health data
Li 2019 A survey on federated learning systems: vision, hype and reality for data privacy and protection J
IEEE transactions on pattern analysis and machine intelligence Miyato 41 1979 2018 10.1109/TPAMI.2018.2858821 Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Kurakin 2016
Moosavi-Dezfooli 2015
IEEE Signal Processing Magazine Li 37 50 2020 Federated learning: Challenges, methods, and future directions
Lo 2020 A systematic literature review on federated machine learning: From a software engineering perspective
Lim 2020 10.1109/COMST.2020.2986024 Federated learning in mobile edge networks: A comprehensive survey
IEEE Access Aledhari 8 140699 2020 10.1109/ACCESS.2020.3013541 Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications
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