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Hijack Vertical Federated Learning Models with Adversarial Embedding. (arXiv:2212.00322v1 [cs.LG])
Dec. 2, 2022, 2:10 a.m. | Pengyu Qiu, Xuhong Zhang, Shouling Ji, Changjiang Li, Yuwen Pu, Xing Yang, Ting Wang
cs.CR updates on arXiv.org arxiv.org
Vertical federated learning (VFL) is an emerging paradigm that enables
collaborators to build machine learning models together in a distributed
fashion. In general, these parties have a group of users in common but own
different features. Existing VFL frameworks use cryptographic techniques to
provide data privacy and security guarantees, leading to a line of works
studying computing efficiency and fast implementation. However, the security of
VFL's model remains underexplored.
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