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Federated Learning for Non-factorizable Models using Deep Generative Prior Approximations
We demonstrate the SIGMA prior's effectiveness on synthetic data and showcase its utility in a real-world example of FL for spatial data, using a conditional autoregressive prior to model spatial dependence across Australia.
Scalable Vertical Federated Learning via Data Augmentation and Amortized Inference
Vertical federated learning (VFL) has emerged as a paradigm for collaborative model estimation across multiple clients, each holding a distinct set of covariates.
Deep Generative Models, Synthetic Tabular Data, and Differential Privacy: An Overview and Synthesis
This article provides a comprehensive synthesis of the recent developments in synthetic data generation via deep generative models, focusing on tabular datasets.
Federated Variational Inference Methods for Structured Latent Variable Models
Federated learning methods enable model training across distributed data sources without data leaving their original locations and have gained increasing interest in various fields.
