FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generation

TL;DR

FedTabDiff introduces a federated diffusion model approach for generating high-quality, privacy-preserving synthetic mixed-type tabular data across multiple decentralized entities, addressing privacy and data complexity challenges.

Contribution

The paper extends diffusion probabilistic models into a federated learning framework specifically for mixed-type tabular data, enabling decentralized high-fidelity data generation.

Findings

Produces synthetic data with high fidelity and utility.

Maintains privacy and data coverage in federated settings.

Effective model aggregation through weighted averaging.

Abstract

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.