Mining Distributed Data using Vertical Federated Learning Review
DOI:
https://doi.org/10.29304/jqcm.2022.14.4.1081Keywords:
federated learning, vertical federated learning, Data mining, Distributted DataAbstract
Federated Learning was designed to allow collaborative learning without revealing raw data as worries about machine learning privacy grew. Vertical Federated Learning (VFL) may be utilized for a distributed dataset with the same sample ID space but differs in feature space. And may be used in a wide variety of real-world contexts when parties have the same set of samples but only have partial attributes. Achieving privacy will be a result of this technique's capacity.
Federated Learning enables different repositories of data to learn a shared model collaboratively and at the same time keep the privacy of each one because of the increasing awareness of large firms compromising on data security and user privacy. To accomplish federated learning, three learning ways were suggested; horizontal federated learning, vertical federated learning, and transfer federated learning.
Vertical federated learning was adopted when data were spread among different parties. However, each one has different features from the others for identical objects. This paper is related to this type of federated learning.
To maximize model performance while maintaining the privacy of dispersed data, we'll create a framework based on vertical federated learning and suitable techniques.
Downloads
Download data is not yet available.
References
Available: http://arxiv.org/abs/2208.01700.
[2] V. Mugunthan, P. Goyal, and L. Kagal, “Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Owners.” 2021, [Online]. Available: http://arxiv.org/abs/2106.05468.
[3] X. Ni, X. Xu, L. Lyu, C. Meng, and W. Wang, “A Vertical Federated Learning Framework for Graph Convolutional Network,” Jun. 2021, doi: 10.48550/arXiv.2106.10056.
[4] J. Zhao, K. Mao, C. Huang, and Y. Zeng, “Utility Optimization of Federated Learning with Differential Privacy,” Discrete Dynamics in Nature and Society, vol. 2021. 2021, doi: 10.1155/2021/3344862.
[5] Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 2. 2019, doi: 10.1145/3298981.
[6] C. Zhang, Y. Xie, H. Bai, B. Yu, W. Li, and Y. Gao, “A survey on federated learning,” Knowledge-Based Syst., vol. 216, no. March, p. 106775, Mar. 2021, doi: 10.1016/j.knosys.2021.106775.
[7] S. Saha and T. Ahmad, “Federated transfer learning: Concept and applications,” Intelligenza Artificiale, vol. 15, no. 1. pp. 35–44, 2021, doi: 10.3233/IA-200075.
[8] Y. Liu, X. Zhang, and L. Wang, “Asymmetrical Vertical Federated Learning.” 2020, [Online]. Available: http://arxiv.org/abs/2004.07427.
[9] J. Sun et al., “Vertical Federated Learning without Revealing Intersection Membership.” 2021, [Online]. Available: http://arxiv.org/abs/2106.05508.
[10] X. Zhang, Z. Ma, A. Wang, H. Mi, and J. Hang, “LstFcFedLear: A LSTM-FC with Vertical Federated Learning Network for Fault Prediction,” Wireless Communications and Mobile Computing, vol. 2021. 2021, doi: 10.1155/2021/2668761.
[11] N. Angelou et al., “Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning.” 2020, [Online]. Available: arxiv:2011.09350.
[12] H. Weng, J. Zhang, F. Xue, T. Wei, S. Ji, and Z. Zong, “Privacy Leakage of Real-World Vertical Federated Learning,” arxiv, 2020, [Online]. Available: https://arxiv.org/pdf/2011.09290.
[13] X. Han, L. Wang, and J. Wu, “Data Valuation for Vertical Federated Learning An Information-Theoretic Approach.pdf.” 2021, doi: 2112.08364.
[14] S. WenJie and S. Xuan, “Vertical federated learning based on DFP and BFGS.” 2021, [Online]. Available: http://arxiv.org/abs/2101.09428.
[15] Y. Liang and Y. Chen, “DVFL: A Vertical Federated Learning Method for Dynamic Data.” 2021, [Online]. Available: http://arxiv.org/abs/2111.03341.
[16] X. Jin, P.-Y. Chen, C.-Y. Hsu, C.-M. Yu, and T. Chen, “CAFE_Catastrophic_Data_Leakage_in_Vertical_Federat (1).pdf.” 2021, doi: 2110.15122.
[17] S. Feng and H. Yu, “Multi-Participant Multi-Class Vertical Federated Learning.” 2020, [Online]. Available: http://arxiv.org/abs/2001.11154.
[18] W. Fang et al., “Large-scale Secure XGB for Vertical Federated Learning,” in International Conference on Information and Knowledge Management, Proceedings, Oct. 2021, pp. 443–452, doi: 10.1145/3459637.3482361.
[19] S. Yang, B. Ren, X. Zhou, and L. Liu, “Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator.” 2019, [Online]. Available: http://arxiv.org/abs/1911.09824.
[20] Q. Zhang et al., “AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization,” in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2021, pp. 3917–3927, doi: 10.1145/3447548.3467169.
[21] C. Liu, Z. Zhou, Y. Shi, J. Pei, L. Chu, and Y. Zhang, “Achieving Model Fairness in Vertical Federated Learning.” 2021, [Online]. Available: http://arxiv.org/abs/2109.08344.
[22] H. Zhu, R. Wang, Y. Jin, and K. Liang, “PIVODL: Privacy-preserving vertical federated learning over distributed labels.” 2021, [Online]. Available: http://arxiv.org/abs/2108.11444.
[23] Z. Wu, Q. Li, and B. He, “Exploiting Record Similarity for Practical Vertical Federated Learning.” 2021, [Online]. Available: http://arxiv.org/abs/2106.06312.
[24] D. Romanini et al., “PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN.” 2021, [Online]. Available: http://arxiv.org/abs/2104.00489.
[25] R. Xu, N. Baracaldo, Y. Zhou, A. Anwar, J. Joshi, and H. Ludwig, “FedV Privacy-Preserving Federated Learning over Vertically.pdf,” 2021, [Online]. Available: https://arxiv.org/pdf/2103.03918.
[26] Q. Zhang, B. Gu, C. Deng, and H. Huang, “Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating,” Mar. 2021, [Online]. Available: http://arxiv.org/abs/2103.00958.
[2] V. Mugunthan, P. Goyal, and L. Kagal, “Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Owners.” 2021, [Online]. Available: http://arxiv.org/abs/2106.05468.
[3] X. Ni, X. Xu, L. Lyu, C. Meng, and W. Wang, “A Vertical Federated Learning Framework for Graph Convolutional Network,” Jun. 2021, doi: 10.48550/arXiv.2106.10056.
[4] J. Zhao, K. Mao, C. Huang, and Y. Zeng, “Utility Optimization of Federated Learning with Differential Privacy,” Discrete Dynamics in Nature and Society, vol. 2021. 2021, doi: 10.1155/2021/3344862.
[5] Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 2. 2019, doi: 10.1145/3298981.
[6] C. Zhang, Y. Xie, H. Bai, B. Yu, W. Li, and Y. Gao, “A survey on federated learning,” Knowledge-Based Syst., vol. 216, no. March, p. 106775, Mar. 2021, doi: 10.1016/j.knosys.2021.106775.
[7] S. Saha and T. Ahmad, “Federated transfer learning: Concept and applications,” Intelligenza Artificiale, vol. 15, no. 1. pp. 35–44, 2021, doi: 10.3233/IA-200075.
[8] Y. Liu, X. Zhang, and L. Wang, “Asymmetrical Vertical Federated Learning.” 2020, [Online]. Available: http://arxiv.org/abs/2004.07427.
[9] J. Sun et al., “Vertical Federated Learning without Revealing Intersection Membership.” 2021, [Online]. Available: http://arxiv.org/abs/2106.05508.
[10] X. Zhang, Z. Ma, A. Wang, H. Mi, and J. Hang, “LstFcFedLear: A LSTM-FC with Vertical Federated Learning Network for Fault Prediction,” Wireless Communications and Mobile Computing, vol. 2021. 2021, doi: 10.1155/2021/2668761.
[11] N. Angelou et al., “Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning.” 2020, [Online]. Available: arxiv:2011.09350.
[12] H. Weng, J. Zhang, F. Xue, T. Wei, S. Ji, and Z. Zong, “Privacy Leakage of Real-World Vertical Federated Learning,” arxiv, 2020, [Online]. Available: https://arxiv.org/pdf/2011.09290.
[13] X. Han, L. Wang, and J. Wu, “Data Valuation for Vertical Federated Learning An Information-Theoretic Approach.pdf.” 2021, doi: 2112.08364.
[14] S. WenJie and S. Xuan, “Vertical federated learning based on DFP and BFGS.” 2021, [Online]. Available: http://arxiv.org/abs/2101.09428.
[15] Y. Liang and Y. Chen, “DVFL: A Vertical Federated Learning Method for Dynamic Data.” 2021, [Online]. Available: http://arxiv.org/abs/2111.03341.
[16] X. Jin, P.-Y. Chen, C.-Y. Hsu, C.-M. Yu, and T. Chen, “CAFE_Catastrophic_Data_Leakage_in_Vertical_Federat (1).pdf.” 2021, doi: 2110.15122.
[17] S. Feng and H. Yu, “Multi-Participant Multi-Class Vertical Federated Learning.” 2020, [Online]. Available: http://arxiv.org/abs/2001.11154.
[18] W. Fang et al., “Large-scale Secure XGB for Vertical Federated Learning,” in International Conference on Information and Knowledge Management, Proceedings, Oct. 2021, pp. 443–452, doi: 10.1145/3459637.3482361.
[19] S. Yang, B. Ren, X. Zhou, and L. Liu, “Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator.” 2019, [Online]. Available: http://arxiv.org/abs/1911.09824.
[20] Q. Zhang et al., “AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization,” in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2021, pp. 3917–3927, doi: 10.1145/3447548.3467169.
[21] C. Liu, Z. Zhou, Y. Shi, J. Pei, L. Chu, and Y. Zhang, “Achieving Model Fairness in Vertical Federated Learning.” 2021, [Online]. Available: http://arxiv.org/abs/2109.08344.
[22] H. Zhu, R. Wang, Y. Jin, and K. Liang, “PIVODL: Privacy-preserving vertical federated learning over distributed labels.” 2021, [Online]. Available: http://arxiv.org/abs/2108.11444.
[23] Z. Wu, Q. Li, and B. He, “Exploiting Record Similarity for Practical Vertical Federated Learning.” 2021, [Online]. Available: http://arxiv.org/abs/2106.06312.
[24] D. Romanini et al., “PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN.” 2021, [Online]. Available: http://arxiv.org/abs/2104.00489.
[25] R. Xu, N. Baracaldo, Y. Zhou, A. Anwar, J. Joshi, and H. Ludwig, “FedV Privacy-Preserving Federated Learning over Vertically.pdf,” 2021, [Online]. Available: https://arxiv.org/pdf/2103.03918.
[26] Q. Zhang, B. Gu, C. Deng, and H. Huang, “Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating,” Mar. 2021, [Online]. Available: http://arxiv.org/abs/2103.00958.
Downloads
Published
2022-12-02
How to Cite
Yassen, M. A., & Muhammed, L. A. (2022). Mining Distributed Data using Vertical Federated Learning Review. Journal of Al-Qadisiyah for Computer Science and Mathematics, 14(4), Comp Page 1–11. https://doi.org/10.29304/jqcm.2022.14.4.1081
Issue
Section
Computer Articles
