참고문헌 (22)

1. Research on Information Security Yang 5 961 2019 AI and data privacy protection: the solution to Federated learning 

2. ACM Transactions on Intelligent Systems and Technology (TIST) Yang 10 1 2019 10.1145/3339474 Federated machine learning: Concept and applications 

   상세보기

3. IEEE transactions on knowledge and data engineering Lu 8 957 1996 10.1109/69.553163 Effective data mining using neural networks 

   상세보기

4. Ateniese 2013 Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers J 

5. Wu 2016 A Methodology for Formalizing Model-Inversion Attacks 2016 IEEE 29th Computer Security Foundations Symposium (CSF) IEEE 

6. IEEE Transactions on Information Forensics and Security Xu 15 911 2019 10.1109/TIFS.2019.2929409 Verifynet: Secure and verifiable federated learning 

   상세보기

7. Zhang 374 2019 August Poisoning attack in federated learning using generative adversarial nets In 

8. Du 222 2004 April Privacy-preserving multivariate statistical analysis: Linear regression and classification In 

9. Bogdanov 192 2008 

10. Hardy 2017 Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption 

11. IEEE Transactions on Information Forensics and Security Aono 13 1333 2017 Privacy-preserving deep learning via additively homomorphic encryption 

12. Choudhury 2019 Differential privacy-enabled federated learning for sensitive health data 

13. Li 2019 A survey on federated learning systems: vision, hype and reality for data privacy and protection J 

14. Kulkarni 2020 10.1109/WorldS450073.2020.9210355 Survey of Personalization Techniques for Federated Learning J 

    

15. 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 

    상세보기

16. Kurakin 2016 

17. Moosavi-Dezfooli 2015 

18. IEEE Signal Processing Magazine Li 37 50 2020 Federated learning: Challenges, methods, and future directions 

    상세보기

19. Lo 2020 A systematic literature review on federated machine learning: From a software engineering perspective 

20. Lim 2020 10.1109/COMST.2020.2986024 Federated learning in mobile edge networks: A comprehensive survey 

    상세보기

21. IEEE Access Aledhari 8 140699 2020 10.1109/ACCESS.2020.3013541 Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications 

    상세보기