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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