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Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with different features about the same set of users jointly train machine learning models without exposing their raw data or model parameters.
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A. Li, H. Peng, L. Zhang, J. Huang, Q. Guo, H. Yu, and Y. Liu, “Fedsdg-fs: Efficient and secure feature selection for vertical federated learning,” IEEE International Conference on Computer Communications , 2023
2023
Closest in time.
T. Castiglia, Y. Zhou, S. Wang, S. Kadhe, N. Baracaldo, and S. Patterson, “Less-vfl: Communication-efficient feature selection for vertical federated learning,” International Conference on Machine Learning , 2023
2023
Closest in time.
R. Fu, Y. Wu, Q. Xu, and M. Zhang, “Feast: A communication-efficient federated feature selection framework for relational data,” Proc. ACM Manag. Data , 2023
2023
Closest in time.
C. Huang, L. Wang, and X. Han, “Vertical federated knowledge transfer via representation distillation for healthcare collaboration networks,” in Proceedings of the ACM Web Conference 2023 , New York, NY, USA, 2023, p. 4188–4199
2023
Closest in time.
H. Gu, J. Luo, Y. Kang, L. Fan, and Q. Yang, “Fedpass: Privacy-preserving vertical federated deep learning with adaptive obfuscation,” in Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23 , 2023, pp. 3759–3767
2023
Closest in time.
2023
Closest in time.
Z. Ren, Y. Kang, L. Fan, L. Yang, T. Fan, Y. Tong, and Q. Yang, “Secureboost hyperparameter tuning via multi-objective federated learning,” International Workshop on Trustworthy Federated Learning in Conjunction with IJCAI , 2023
2023
Closest in time.