Faithful Federated Tabular Data Synthesis

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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.