Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Data

arXiv:2403.03871v1 Announce Type: new
Abstract: Vertical Federated Learning (VFL) is an emergent distributed machine learning paradigm wherein owners of disjoint features of a common set of entities collaborate to learn a global model without sharing data. In VFL, a host client owns data labels for each entity and learns a final representation based on intermediate local representations from all guest clients. Therefore, the host is a single point of failure and label feedback can be used by malicious guest clients …

abstract arxiv client cs.dc cs.lg data distributed features federated learning global labels learn machine machine learning paradigm practical set sharing data training type