mlsanityData to 7 Oct 2026

Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis

arXiv 2412.16083 ↗First posted 20 Dec 2024cs.LG q-fin.STcited by 2

On the map: Diffusion models → Robustness, privacy and adversarial aspects

Abstract (arXiv)

The increasing demand for privacy-preserving data analytics in various domains necessitates solutions for synthetic data generation that rigorously uphold privacy standards. We introduce the DP-FedTabDiff framework, a novel integration of Differential Privacy, Federated Learning and Denoising Diffusion Probabilistic Models designed to generate high-fidelity synthetic tabular data. This framework ensures compliance with privacy regulations while maintaining data utility. We demonstrate the effectiveness of DP-FedTabDiff on multiple real-world mixed-type tabular datasets, achieving significant improvements in privacy guarantees without compromising data quality. Our empirical evaluations reveal the optimal trade-offs between privacy budgets, client configurations, and federated optimization strategies. The results affirm the potential of DP-FedTabDiff to enable secure data sharing and analytics in highly regulated domains, paving the way for further advances in federated learning and privacy-preserving data synthesis.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models. Builds on 40 of its 56 references that are in the map. First posted on arXiv 20 Dec 2024.

Builds on

40 of 56 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024Training Diffusion Models with Federated Learning18 Jun
  2. 2024FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generation11 Jan
  3. 2023Phoenix: A Federated Generative Diffusion Model7 Jun
  4. 2022Differentially Private Diffusion Models18 Oct

Other references · 36

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  5. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  6. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  7. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  8. 2019Advances and Open Problems in Federated Learningcited by 3,021
  9. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  10. 2016Deep Learning with Differential Privacycited by 2,682
  11. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  12. 2020Adaptive Federated Optimizationcited by 1,036
24 more
  1. 2022Diffusion Models in Vision: A Surveycited by 1,008
  2. 2019Modeling Tabular data using Conditional GANcited by 695
  3. 2021Argmax Flows and Multinomial Diffusion: Learning Categorical Distributionscited by 566
  4. 2018ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Modelscited by 475
  5. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  6. 2021Opacus: User-Friendly Differential Privacy Library in PyTorchcited by 375
  7. 2022TabDDPM: Modelling Tabular Data with Diffusion Modelscited by 373
  8. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  9. 2019SCAFFOLD: Stochastic Controlled Averaging for Federated Learningcited by 174
  10. 2021CTAB-GAN: Effective Table Data Synthesizingcited by 156
  11. 2020GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generatorscited by 117
  12. 2019Generative Models for Effective ML on Private, Decentralized Datasetscited by 85
  13. 2020Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learningcited by 67
  14. 2020Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applicationscited by 58
  15. 2020Differentially Private Synthetic Medical Data Generation using Convolutional GANscited by 52
  16. 2022A Unified Framework for Quantifying Privacy Risk in Synthetic Datacited by 45
  17. 2023FinDiff: Diffusion Models for Financial Tabular Data Generationcited by 41
  18. 2021FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumoniacited by 22
  19. 2022On the Privacy Properties of GAN-generated Samplescited by 16
  20. 2021SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learningcited by 15
  21. 2019Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questionscited by 12
  22. 2022Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Auditscited by 8
  23. 2021Synthetic Data Generation for Fraud Detection using GANscited by 7
  24. 2024A Systematic Review of Federated Generative Modelscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Collaborative Synthetic Data for Privacy-Preserving Financial Fraud Detection Across Organizational Siloscited by 0
  2. 2026Beyond Uniform Compression: Budgeted Transmission Allocation for Extreme Federated Learningcited by 0

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  5. 2024Differentially Private Tabular Data Synthesis using Large Language Models
  6. 2024DP-TLDM: Differentially Private Tabular Latent Diffusion Model
  7. 2026Differentially Private Synthetic Data via APIs 4: Tabular Data
  8. 2025Personalized Federated Training of Diffusion Models with Privacy Guarantees