KDk: A Defense Mechanism Against Label Inference Attacks in Vertical Federated Learning

arXiv:2404.12369v1 Announce Type: new
Abstract: Vertical Federated Learning (VFL) is a category of Federated Learning in which models are trained collaboratively among parties with vertically partitioned data. Typically, in a VFL scenario, the labels of the samples are kept private from all the parties except for the aggregating server, that is the label owner. Nevertheless, recent works discovered that by exploiting gradient information returned by the server to bottom models, with the knowledge of only a small set of auxiliary …

abstract arxiv attacks cs.cr cs.lg data defense federated learning inference labels parties samples type