Federated method generates accurate synthetic mixed-type tabular data
Faithful Federated Tabular Data Synthesis
Machine Learning
Summary
Creating fake but realistic data on multiple computers without sharing private information is hard when the data has both numbers and categories. The authors propose a new two-step way to do this, making sure the fake data matches the real data well. They change categories into numbers temporarily to treat all data the same, which helps in generating good fake data. They also show this method works well in tests and improves tasks for everyone involved.
What this means in practice
- •For data science teams: Create synthetic tabular datasets with mixed data types across distributed sources without exposing private data to enhance modeling and analysis.
- •For healthcare data engineers: Generate faithful synthetic patient records from multiple institutions to improve machine learning model training while preserving data privacy.
Authors
Zilong Cao, Hai Zhang, Xuan Bi
Abstract
Synthetic data is an important data source in the AI era. This paper studies the generation of mixed-type tabular synthetic data in federated settings, where two key challenges arise: ensuring distributional fidelity and effectively handling mixed-type data. We propose Federated Integrative Two-Step Synthesis (FITS), a framework for mixed-type tabular data synthesis. At its core is a two-step federated algorithm for estimating conditional cumulative distribution functions (CDFs) using local linear regression. Building on this algorithm, we develop a federated synthesis procedure based on the probability chain rule to ensure distributional fidelity. To handle mixed-type data, FITS introduces a continualization method that lifts discrete variables into a continuous space and recovers their discrete distributions without loss at both the density-function and CDF levels. This transformation provides a unified continuous representation of mixed-type variables, facilitating the application of local linear regression and synthetic data generation. Our theoretical analysis characterizes the conditional density and cumulative distribution functions of the continualized mixed-type variables and establishes the uniform convergence of the resulting federated conditional CDF estimators. Experiments demonstrate that FITS-generated synthetic data improves downstream task performance for all clients, with the benefits depending on how the synthetic data is used.