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

• URL: http://arxiv.org/abs/2303.18178v1
• Date: Tue, 28 Mar 2023 19:58:28 GMT
• Title: Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties
• Authors: Jingwei Sun, Zhixu Du, Anna Dai, Saleh Baghersalimi, Alireza Amirshahi, David Atienza, Yiran Chen
• Abstract summary: Vertical federated learning (VFL) enables a service provider (i.e., active party) who owns labeled features to collaborate with passive parties who possess auxiliary features to improve model performance. Existing VFL approaches have two major vulnerabilities when passive parties unexpectedly quit in the deployment phase of VFL. We propose textbfParty-wise Dropout to improve the VFL model's robustness against the unexpected exit of passive parties and a defense method called textbfDIMIP to protect the active party's IP in the deployment phase.
• Score: 29.229942556038676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vertical federated learning (VFL) enables a service provider (i.e., active party) who owns labeled features to collaborate with passive parties who possess auxiliary features to improve model performance. Existing VFL approaches, however, have 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. In this paper, we propose \textbf{Party-wise Dropout} to improve the VFL model's robustness against the unexpected exit of passive parties and a defense method called \textbf{DIMIP} to protect the active party's IP in the deployment phase. We evaluate our proposed methods on multiple datasets against different inference attacks. The results show that Party-wise Dropout effectively maintains model performance after the passive party quits, and DIMIP successfully disguises label information from the passive party's feature extractor, thereby mitigating IP leakage.

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