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PlugVFL: Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties

Publication ,  Conference
Sun, J; Du, Z; Dai, A; Baghersalimi, S; Amirshahi, A; Atienza, D; Chen, Y
Published in: Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024
January 1, 2024

In federated learning systems, the unexpected quitting of participants is inevitable. Such quittings generally do not incur serious consequences in horizontal federated learning (HFL), but they do damage to vertical federated learning (VFL), which has been underexplored in previous research. In this paper, we show that there are two major vulnerabilities when passive parties unexpectedly quit in the deployment phase of VFL - severe performance degradation and intellectual property (IP) leakage of the active party's labels. To solve these issues, we design PlugVFL to improve the VFL model's robustness against the unexpected exit of passive parties and protect the active party's IP in the deployment phase simultaneously. We evaluate our framework on multiple datasets against different inference attacks. The results show that PlugVFL effectively maintains model performance after the passive party quits and successfully disguises label information from the passive party's feature extractor, thereby mitigating IP leakage.

Duke Scholars

Published In

Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024

DOI

Publication Date

January 1, 2024

Start / End Page

1124 / 1133
 

Citation

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Sun, J., Du, Z., Dai, A., Baghersalimi, S., Amirshahi, A., Atienza, D., & Chen, Y. (2024). PlugVFL: Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties. In Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024 (pp. 1124–1133). https://doi.org/10.1109/BigData62323.2024.10825534
Sun, J., Z. Du, A. Dai, S. Baghersalimi, A. Amirshahi, D. Atienza, and Y. Chen. “PlugVFL: Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties.” In Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024, 1124–33, 2024. https://doi.org/10.1109/BigData62323.2024.10825534.
Sun J, Du Z, Dai A, Baghersalimi S, Amirshahi A, Atienza D, et al. PlugVFL: Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties. In: Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024. 2024. p. 1124–33.
Sun, J., et al. “PlugVFL: Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties.” Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024, 2024, pp. 1124–33. Scopus, doi:10.1109/BigData62323.2024.10825534.
Sun J, Du Z, Dai A, Baghersalimi S, Amirshahi A, Atienza D, Chen Y. PlugVFL: Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties. Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024. 2024. p. 1124–1133.

Published In

Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024

DOI

Publication Date

January 1, 2024

Start / End Page

1124 / 1133