Hijack Vertical Federated Learning Models As One Party

arXiv:2212.00322v2 Announce Type: replace-cross
Abstract: 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.

arxiv build cryptographic cs.ai cs.cr cs.lg data data privacy distributed emerging fashion features federated federated learning frameworks general hijack machine machine learning machine learning models own paradigm party privacy techniques