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Vertical federated learning (VFL), a variant of Federated Learning (FL), has recently drawn increasing attention as the VFL matches the enterprises' demands of leveraging more valuable features to achieve better model performance.
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Y. Liu, Y. Kang, C. Xing, T. Chen, and Q. Yang, “A Secure Federated Transfer Learning Framework,” IEEE Intelligent Systems , vol. 35, no. 4, pp. 70–82, Jul. 2020
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Y. Liu, Y. Kang, C. Xing, T. Chen, and Q. Yang, “Secure Federated Transfer Learning,” IEEE Intelligent Systems , vol. 35, no. 4, pp. 70–82, Jul. 2020
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S. Feng and H. Yu, “Multi-Participant Multi-Class Vertical Federated Learning,” Jan. 2020
2020
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K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum Contrast for Unsupervised Visual Representation Learning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 9729–9738
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J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. H. Richemond, E. Buchatskaya, C. Doersch, B. A. Pires, Z. D. Guo, M. G. Azar, B. Piot, K. Kavukcuoglu, R. Munos, and M. Valko, “Bootstrap your own latent: A new approach to self-supervised Learning,” Advances in neural information processing systems , vol. 33, pp. 21 271–21 284, 2020
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L. Fan, K. W. Ng, C. Ju, T. Zhang, C. Liu, C. S. Chan, and Q. Yang, Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks . Cham: Springer International Publishing, 2020, pp. 32–50
2020
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2021
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Y. Yang, X. Ye, and T. Sakurai, “Multi-View Federated Learning with Data Collaboration,” in 2022 14th International Conference on Machine Learning and Computing (ICMLC) , ser. ICMLC 2022. New York, NY, USA: Association for Computing Machinery, Jun. 2022, pp. 178–183
2022
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Z. Ren, L. Yang, and K. Chen, “Improving Availability of Vertical Federated Learning: Relaxing Inference on Non-overlapping Data,” ACM Transactions on Intelligent Systems and Technology , vol. 13, no. 4, pp. 58:1–58:20, Jun. 2022
2022
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W. Li, Q. Xia, H. Cheng, K. Xue, and S.-T. Xia, “Vertical Semi-Federated Learning for Efficient Online Advertising,” Sep. 2022
2022
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S. Feng, B. Li, H. Yu, Y. Liu, and Q. Yang, “Semi-Supervised Federated Heterogeneous Transfer Learning,” Knowledge-Based Systems , vol. 252, p. 109384, Sep. 2022
2022
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2021
Cited alongside, same era.
K. Cheng, T. Fan, Y. Jin, Y. Liu, T. Chen, D. Papadopoulos, and Q. Yang, “SecureBoost: A Lossless Federated Learning Framework,” IEEE Intelligent Systems , vol. 36, no. 6, pp. 87–98, 2021
2021
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Q. Li, B. He, and D. Song, “Model-Contrastive Federated Learning,” Mar. 2021
2021
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X. Mu, Y. Shen, K. Cheng, X. Geng, J. Fu, T. Zhang, and Z. Zhang, “FedProc: Prototypical Contrastive Federated Learning on Non-IID data,” Sep. 2021
2021
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W. Zhuang, X. Gan, Y. Wen, S. Zhang, and S. Yi, “Collaborative Unsupervised Visual Representation Learning from Decentralized Data,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 4912–4921
2021
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C. He, Z. Yang, E. Mushtaq, S. Lee, M. Soltanolkotabi, and S. Avestimehr, “SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision,” in International Workshop on Trustable, Verifiable and Auditable Federated Learning in Conjunction with AAAI 2022 (FL-AAAI-22) , Oct. 2021
2021
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X. Chen and K. He, “Exploring simple siamese representation learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 15 750–15 758
2021
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T. Yao, X. Yi, D. Z. Cheng, F. Yu, T. Chen, A. Menon, L. Hong, E. H. Chi, S. Tjoa, J. Kang, and E. Ettinger, “Self-supervised Learning for Large-scale Item Recommendations,” in Proceedings of the 30th ACM International Conference on Information & Knowledge Management , 2021, pp. 4321–4330
2021
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Y. Kang, Y. He, J. Luo, T. Fan, Y. Liu, and Q. Yang, “Privacy-preserving federated adversarial domain adaptation over feature groups for interpretability,” IEEE Transactions on Big Data , pp. 1–12, 2022
2022
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——, “Semi-Supervised Federated Heterogeneous Transfer Learning,” Knowledge-Based Systems , vol. 252, p. 109384, Sep. 2022
2022
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Y. Tan, G. Long, J. Ma, L. Liu, T. Zhou, and J. Jiang, “Federated Learning from Pre-Trained Models: A Contrastive Learning Approach,” Sep. 2022
2022
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S. Han, S. Park, F. Wu, S. Kim, C. Wu, X. Xie, and M. Cha, “FedX: Unsupervised Federated Learning with Cross Knowledge Distillation,” Jul. 2022
2022
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O. Li, J. Sun, X. Yang, W. Gao, H. Zhang, J. Xie, V. Smith, and C. Wang, “Label leakage and protection in two-party split learning,” in International Conference on Learning Representations , 2022. [Online]. Available: https://openreview.net/forum?id=cOtBRgsf2fO
2022
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C. Fu, X. Zhang, S. Ji, J. Chen, J. Wu, S. Guo, J. Zhou, A. X. Liu, and T. Wang, “Label inference attacks against vertical federated learning,” in 31st USENIX Security Symposium (USENIX Security 22) , 2022
2022
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T. Zou, Y. Liu, Y. Kang, W. Liu, Y. He, Z. Yi, Q. Yang, and Y. Zhang, “Defending batch-level label inference and replacement attacks in vertical federated learning,” IEEE Transactions on Big Data , pp. 1–12, jul 2022
2022
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2022
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Y. Wu, Y. Kang, J. Luo, Y. He, and Q. Yang, “Fedcg: Leverage conditional gan for protecting privacy and maintaining competitive performance in federated learning,” in Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22 . International Joint Conferences on Artificial Intelligence Organization, 2022
2022
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D. Bahri, H. Jiang, Y. Tay, and D. Metzler, “SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption,” in International Conference on Learning Representations , Jun. 2022
2022
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Y. Liu, X. Zhang, Y. Kang, L. Li, T. Chen, M. Hong, and Q. Yang, “FedBCD: A Communication-Efficient Collaborative Learning Framework for Distributed Features,” IEEE Transactions on Signal Processing , 2022
2022
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2022
Closest in time.
C.-j. 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 , ser. WWW ’23. New York, NY, USA: Association for Computing Machinery, Apr. 2023, pp. 4188–4199
2023
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Y. Liu, X. Liang, J. Luo, Y. He, T. Chen, Q. Yao, and Q. Yang, “Cross-Silo Federated Neural Architecture Search for Heterogeneous and Cooperative Systems,” in Federated and Transfer Learning , ser. Adaptation, Learning, and Optimization, R. Razavi-Far, B. Wang, M. E. Taylor, and Q. Yang, Eds. Cham: Springer International Publishing, 2023, pp. 57–86
2023
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