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Vertical federated learning trains models from feature-partitioned datasets across multiple clients, who collaborate without sharing their local data.
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Multi-participant multi-class vertical federated learning
S. Feng and H. Yu · 2020
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A secure federated transfer learning framework
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Advances and open problems in federated learning, 2021
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Practical lossless federated singular vector decomposition over billion-scale data, 2022
D. Chai, L. Wang, J. Zhang, L. Yang, S. Cai, K. Chen, and Q. Yang · 2022
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Vertical federated learning-based feature selection with non-overlapping sample utilization
S. Feng · 2022
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Vertical semi-federated learning for efficient online advertising
W. Li, Q. Xia, H. Cheng, K. Xue, and S.-T. Xia · 2023
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Communication-efficient vertical federated learning with limited overlapping samples
J. Sun, Z. Xu, D. Yang, V. Nath, W. Li, C. Zhao, D. Xu, Y. Chen, and H. R. Roth · 2023
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Fault tolerant serverless vfl over dynamic device environment, 2024
S. Ganguli, Z. Zhou, C. G. Brinton, and D. I. Inouye · 2024
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Complementary knowledge distillation for robust and privacy-preserving model serving in vertical federated learning
D. Gao, S. Wan, L. Fan, X. Yao, and Q. Yang · 2024
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A hybrid self-supervised learning framework for vertical federated learning
Y. He, Y. Kang, X. Zhao, J. Luo, L. Fan, Y. Han, and Q. Yang · 2024
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An Extensive Data Processing Pipeline for MIMIC-IV
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FedBCD: A communication-efficient collaborative learning framework for distributed features
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Improving availability of vertical federated learning: Relaxing inference on non-overlapping data
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Multi-view federated learning with data collaboration
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Vertical federated knowledge transfer via representation distillation for healthcare collaboration networks
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Privacy-preserving federated adversarial domain adaptation over feature groups for interpretability
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Vertical federated learning: Concepts, advances, and challenges
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PlugVFL: Robust and IP-protecting vertical federated learning against unexpected quitting of parties, 2024
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A distributed generative adversarial network for data augmentation under vertical federated learning
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Review for handling missing data with special missing mechanism
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Communication-efficient vertical federated learning via compressed error feedback
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