Quadratic Functional Encryption for Secure Training in Vertical Federated Learning

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K. Yang, T. Fan, T. Chen, Y. Shi, and Q. Yang, “A quasi-newton method based vertical federated learning framework for logistic regression,” ArXiv , vol. abs/1912.00513, 2019

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S. Yang, B. Ren, X. Zhou, and L. Liu, “Parallel distributed logistic regression for vertical federated learning without third-party coordinator,” arXiv preprint arXiv:1911.09824 , 2019

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B. Libert and R. ŢiŢiu, “Multi-client functional encryption for linear functions in the standard model from lwe,” in Advances in Cryptology – ASIACRYPT 2019 , S. D. Galbraith and S. Moriai, Eds. Cham: Springer International Publishing, 2019, pp. 520–551

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D. He, R. Du, S. Zhu, M. Zhang, K. Liang, and S. Chan, “Secure logistic regression for vertical federated learning,” IEEE Internet Computing , vol. 26, no. 2, pp. 61–68, 2021

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H. Sun, Z. Wang, Y. Huang, and J. Ye, “Privacy-preserving vertical federated logistic regression without trusted third-party coordinator,” in 2022 The 6th International Conference on Machine Learning and Soft Computing , ser. ICMLSC 2022. New York, NY, USA: Association for Computing Machinery, 2022, p. 132–138. [Online]. Available: https://doi.org/10.1145/3523150.3523171

2022

Later among the works it cites.

D. Zhao, M. Yao, W. Wang, H. He, and X. Jin, “Ntp-vfl - a new scheme for non-3rd party vertical federated learning,” in 2022 14th International Conference on Machine Learning and Computing (ICMLC) , ser. ICMLC 2022. New York, NY, USA: Association for Computing Machinery, 2022, p. 134–139. [Online]. Available: https://doi.org/10.1145/3529836.3529841

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