FedTabDiff: Federated Learning of Diffusion Probabilistic Models for
Synthetic Mixed-Type Tabular Data Generation
- URL: http://arxiv.org/abs/2401.06263v1
- Date: Thu, 11 Jan 2024 21:17:50 GMT
- Title: FedTabDiff: Federated Learning of Diffusion Probabilistic Models for
Synthetic Mixed-Type Tabular Data Generation
- Authors: Timur Sattarov, Marco Schreyer, Damian Borth
- Abstract summary: We introduce textitFederated Tabular Diffusion (FedTabDiff) for generating high-fidelity mixed-type tabular data without centralized access to the original datasets.
FedTabDiff realizes a decentralized learning scheme that permits multiple entities to collaboratively train a generative model while respecting data privacy and locality.
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.
- Score: 5.824064631226058
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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.
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