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

Jingwei Sun, Zhixu Du, Anna Dai, Saleh Baghersalimi, Alireza Amirshahi, Qilin Zheng, David Atienza, Yiran Chen

Primary Area: representation learning for computer vision, audio, language, and other modalities

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Keywords: federated learning

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Abstract: 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 bring damage to vertical federated learning (VFL), which is 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.

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Submission Number: 3822