Realistic synthetic tabular data generation encounters significant challenges
in preserving privacy, especially when dealing with sensitive information in
domains like finance and healthcare. In this paper, we introduce
\textit{Federated Tabular Diffusion} (FedTabDiff) for generating high-fidelity
mixed-type tabular data without centralized access to the original tabular
datasets. Leveraging the strengths of \textit{Denoising Diffusion Probabilistic
Models} (DDPMs), our approach addresses the inherent complexities in tabular
data, such as mixed attribute types and implicit relationships. More
critically, FedTabDiff realizes a decentralized learning scheme that permits
multiple entities to collaboratively train a generative model while respecting
data privacy and locality. We extend DDPMs into the federated setting for
tabular data generation, which includes a synchronous update scheme and
weighted averaging for effective model aggregation. Experimental evaluations on
real-world financial and medical datasets attest to the framework's capability
to produce synthetic data that maintains high fidelity, utility, privacy, and
coverage.

通过引入 Federated Tabular Diffusion (FedTabDiff)，旨在无需中心化访问原始数据集的情况下生成高保真度的混合类型表格数据，从而解决了保护隐私、混合属性类型和隐含关系等表格数据固有的复杂性问题。该方法采用去噪扩散概率模型 (DDPMs) 的优点，并实现了一种允许多个实体协同训练生成模型的分散学习方案，同时尊重数据隐私和本地性。FedTabDiff 还在联邦设置中扩展了 DDPMs，其中包括同步更新方案和加权平均以实现有效的模型聚合。对真实世界的金融和医疗数据集的实验评估证明了该框架能够产生保持高保真度、效用度、隐私和覆盖度的合成数据。