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Vertical federated learning (VFL) is an emerging paradigm that enables collaborators to build machine learning models together in a distributed fashion.
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2021
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2021
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2021
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2022
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P. Qiu, X. Zhang, S. Ji, T. Du, Y. Pu, J. Zhou, and T. Wang, “Your labels are selling you out: Relation leaks in vertical federated learning,” IEEE Transactions on Dependable and Secure Computing , pp. 1–16, 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) . Boston, MA: USENIX Association, Aug. 2022, pp. 1397–1414
2022
Closest in time.
P. Qiu, X. Zhang, S. Ji, T. Du, Y. Pu, J. Zhou, and T. Wang, “Your labels are selling you out: Relation leaks in vertical federated learning,” IEEE Transactions on Dependable and Secure Computing , pp. 1–16, 2022
2022
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Y. Djenouri, A. Belhadi, G. Srivastava, U. Ghosh, P. Chatterjee, and J. C.-W. Lin, “Fast and accurate deep learning framework for secure fault diagnosis in the industrial internet of things,” IEEE Internet of Things Journal , vol. 10, no. 4, pp. 2802–2810, 2023
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2023
Closest in time.
S. K. Singh, L. T. Yang, and J. H. Park, “Fusionfedblock: Fusion of blockchain and federated learning to preserve privacy in industry 5.0,” Information Fusion , vol. 90, pp. 233–240, 2023
2023
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Y. Djenouri, A. Belhadi, G. Srivastava, U. Ghosh, P. Chatterjee, and J. C.-W. Lin, “Fast and accurate deep learning framework for secure fault diagnosis in the industrial internet of things,” IEEE Internet of Things Journal , vol. 10, no. 4, pp. 2802–2810, 2023
2023
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H. Gao, N. He, and T. Gao, “Sverifl: Successive verifiable federated learning with privacy-preserving,” Information Sciences , vol. 622, pp. 98–114, 2023
2023
Closest in time.
S. K. Singh, L. T. Yang, and J. H. Park, “Fusionfedblock: Fusion of blockchain and federated learning to preserve privacy in industry 5.0,” Information Fusion , vol. 90, pp. 233–240, 2023
2023
Closest in time.