{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.33403",
  "author_total": 3,
  "authors": [
    {
      "name": "Zilong Cao"
    },
    {
      "name": "Hai Zhang"
    },
    {
      "name": "Xuan Bi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2kdmeshgcyt3a.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.33403",
  "fulltext_url": null,
  "id": "2kdmeshgcyt3a",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.33403",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "math.ST"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "arxiv",
      "stat.TH"
    ],
    [
      "openalex",
      "1702|Privacy-Preserving Technologies in Data"
    ]
  ],
  "openalex": "W7223947022",
  "pdf_url": "https://arxiv.org/pdf/2610.33403",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-24",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2kdmeshgcyt3a.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:40Z",
      "native_id": "2610.33403",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.33403"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.ST",
      "subfield": "statistics"
    }
  ],
  "title": "Faithful Federated Tabular Data Synthesis",
  "title_text": "Faithful Federated Tabular Data Synthesis",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:49Z",
  "url": "https://research.draconitic.com/paper/2kdmeshgcyt3a",
  "venue": "arXiv",
  "versions": []
}