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FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generation (2401.06263v1)

Published 11 Jan 2024 in cs.LG

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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Federated Learning and Synthetic Data

In the domain of data analytics, privacy-preserving techniques are of utmost importance, particularly when dealing with sensitive information from sectors like finance and healthcare. A groundbreaking approach to tackle this issue is through the use of synthetic data, which not only aids in preserving privacy but also facilitates data sharing, compliance with regulations, and a deeper analysis without disclosure risks. Synthetic data is a generative construct that mirrors the statistical properties of real data, allowing for insights while protecting individual data points.

Innovations in Federated Learning

A novel framework named Federated Tabular Diffusion, or FedTabDiff, has been introduced for generating mixed-type tabular data—a composite of categorical, numerical, and ordinal data distributions. This model advances the fusion of Denoising Diffusion Probabilistic Models (DDPMs) with federated learning. DDPMs are well-regarded for producing high-quality synthetic images, while federated learning (FL) circumvents the need to consolidate sensitive data into one location, instead allowing multiple parties to contribute to a joint model while retaining their data locally.

Privacy-Preserving Data Generation

FedTabDiff is remarkable for its approach to generative modeling that respects data privacy concerns. It employs a system where participants of a federated network, referred to as clients, train local models on their available data. Periodically, these local model parameters are communicated to a central server, which aggregates them to enhance a global model, and then redistributes the updated version to all clients. This cycle ensures no raw data ever leaves its original repository, thus maintaining confidentiality and integrity.

Evaluation and Results

FedTabDiff underwent thorough evaluation using financial and healthcare datasets. It showed proficiency in data synthesis while ensuring compliance with privacy requirements. The model consistently outperformed non-federated counterparts across a range of metrics including fidelity (the resemblance to the original data), utility (applicability for downstream tasks), and coverage (diversity representation). Significantly, it also rated high on privacy, eliminating worries over data leakage.

Conclusion and Future Envisage

The superiority of the FedTabDiff model in producing privacy-compliant, high-fidelity, and useful synthetic tabular data is a remarkable achievement in federated learning. The methodology employed by FedTabDiff opens doors to more collaborative and responsible use of AI, especially in sensitive fields. Prospective research directions may include enhancing federated learning protocols and refining diffusion model processes, contributing to the acceleration of secure AI implementations.

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Authors (3)
  1. Timur Sattarov (11 papers)
  2. Marco Schreyer (14 papers)
  3. Damian Borth (64 papers)
Citations (2)
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