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Scalable Vertical Federated Learning via Data Augmentation and Amortized Inference
May 8, 2024, 4:42 a.m. | Conor Hassan, Matthew Sutton, Antonietta Mira, Kerrie Mengersen
cs.LG updates on arXiv.org arxiv.org
Abstract: Vertical federated learning (VFL) has emerged as a paradigm for collaborative model estimation across multiple clients, each holding a distinct set of covariates. This paper introduces the first comprehensive framework for fitting Bayesian models in the VFL setting. We propose a novel approach that leverages data augmentation techniques to transform VFL problems into a form compatible with existing Bayesian federated learning algorithms. We present an innovative model formulation for specific VFL scenarios where the joint …
abstract arxiv augmentation bayesian collaborative cs.lg data federated learning framework inference multiple novel paper paradigm scalable set stat.co stat.me stat.ml type via
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