Label inference attacks against vertical federated learning
31st USENIX security symposium (USENIX Security 22), 2022•usenix.org
As the initial variant of federated learning (FL), horizontal federated learning (HFL) applies to
the situations where datasets share the same feature space but differ in the sample space,
eg, the collaboration between two regional banks, while trending vertical federated learning
(VFL) deals with the cases where datasets share the same sample space but differ in the
feature space, eg, the collaboration between a bank and an e-commerce platform.
the situations where datasets share the same feature space but differ in the sample space,
eg, the collaboration between two regional banks, while trending vertical federated learning
(VFL) deals with the cases where datasets share the same sample space but differ in the
feature space, eg, the collaboration between a bank and an e-commerce platform.
Abstract
As the initial variant of federated learning (FL), horizontal federated learning (HFL) applies to the situations where datasets share the same feature space but differ in the sample space, eg, the collaboration between two regional banks, while trending vertical federated learning (VFL) deals with the cases where datasets share the same sample space but differ in the feature space, eg, the collaboration between a bank and an e-commerce platform.
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