FateWithShellmiaoComment/Fate/doc/resources

Materials

中文

Speech and Conference

• 2021. Professor Yang's Seminar Presentation: An Introducton to Federated Learning (2021)
• 2019. SecureBoost-ijcai2019-workshop
• 2019. GDPR_Data_Shortage_and_AI-AAAI_2019_PPT

Paper

1. 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.
2. Liu Y, Fan T, Chen T, et al. FATE: An industrial grade platform for collaborative learning with data protection[J]. Journal of Machine Learning Research, 2021, 22(226): 1-6
3. Cheng K, Fan T, Jin Y, et al. Secureboost: A lossless federated learning framework[J]. IEEE Intelligent Systems, 2021.
4. Chen W, Ma G, Fan T, et al. SecureBoost+: A High Performance Gradient Boosting Tree Framework for Large Scale Vertical Federated Learning[J]. arXiv preprint arXiv:2110.10927, 2021.
5. Zhang Q, Wang C, Wu H, et al. GELU-Net: A Globally Encrypted, Locally Unencrypted Deep Neural Network for Privacy-Preserved Learning[C]//IJCAI. 2018: 3933-3939.
6. Zhang Y, Zhu H. Additively Homomorphical Encryption based Deep Neural Network for Asymmetrically Collaborative Machine Learning[J]. arXiv preprint arXiv:2007.06849, 2020.
7. 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.
8. 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.
9. Chen C, Zhou J, Wang L, et al. When homomorphic encryption marries secret sharing: Secure large-scale sparse logistic regression and applications in risk control[C]//Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2021: 2652-2662.