mlsanityData to 7 Oct 2026

A Systematic Review of Federated Generative Models

arXiv 2405.16682 ↗First posted 26 May 2024cs.LG cs.CL cs.CRcited by 5

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Abstract (arXiv)

Federated Learning (FL) has emerged as a solution for distributed systems that allow clients to train models on their data and only share models instead of local data. Generative Models are designed to learn the distribution of a dataset and generate new data samples that are similar to the original data. Many prior works have tried proposing Federated Generative Models. Using Federated Learning and Generative Models together can be susceptible to attacks, and designing the optimal architecture remains challenging. This survey covers the growing interest in the intersection of FL and Generative Models by comprehensively reviewing research conducted from 2019 to 2024. We systematically compare nearly 100 papers, focusing on their FL and Generative Model methods and privacy considerations. To make this field more accessible to newcomers, we highlight the state-of-the-art advancements and identify unresolved challenges, offering insights for future research in this evolving field.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 63 of its 138 references that are in the map. First posted on arXiv 26 May 2024.

Builds on

63 of 138 references.

References · 63

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2022Generative Adversarial Networkscited by 5,603
  4. 2019Advances and Open Problems in Federated Learningcited by 3,021
  5. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  6. 2021Variational Diffusion Modelscited by 1,145
  7. 2018Federated Learning with Non-IID Datacited by 1,099
  8. 2022Diffusion Models in Vision: A Surveycited by 1,008
  9. 2017Generative Adversarial Networks: An Overviewcited by 588
  10. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  11. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 479
  12. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
38 more
  1. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  2. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 380
  3. 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
  4. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  5. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 282
  6. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 266
  7. 2022A Survey on Generative Diffusion Modelcited by 238
  8. 2021Evaluating Gradient Inversion Attacks and Defenses in Federated Learningcited by 196
  9. 2023Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attackscited by 189
  10. 2020LDP-Fed: Federated Learning with Local Differential Privacycited by 129
  11. 2021FedMix: Approximation of Mixup under Mean Augmented Federated Learningcited by 108
  12. 2021DENSE: Data-Free One-Shot Federated Learningcited by 100
  13. 2021Noise Estimation for Generative Diffusion Modelscited by 94
  14. 2020FedGAN: Federated Generative Adversarial Networks for Distributed Datacited by 89
  15. 2019Generative Models for Effective ML on Private, Decentralized Datasetscited by 85
  16. 2022Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challengescited by 81
  17. 2020Local and Central Differential Privacy for Robustness and Privacy in Federated Learningcited by 72
  18. 2022FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learningcited by 65
  19. 2017Adversarial examples for generative modelscited by 58
  20. 2022Vertical Federated Learning: Challenges, Methodologies and Experimentscited by 55
  21. 2022Do Gradient Inversion Attacks Make Federated Learning Unsafe?cited by 44
  22. 2017Differentially Private Mixture of Generative Neural Networkscited by 39
  23. 2021FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learningcited by 39
  24. 2022Federated Learning with GAN-based Data Synthesis for Non-IID Clientscited by 25
  25. 2021Adversarial Attacks Against Deep Generative Models on Data: A Surveycited by 23
  26. 2021FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumoniacited by 22
  27. 2021Factor Analysis, Probabilistic Principal Component Analysis, Variational Inference, and Variational Autoencoder: Tutorial and Surveycited by 21
  28. 2022FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesiscited by 21
  29. 2023A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiencycited by 21
  30. 2021Federated Learning for COVID-19 Detection with Generative Adversarial Networks in Edge Cloud Computingcited by 18
  31. 2021SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learningcited by 15
  32. 2022DPD-fVAE: Synthetic Data Generation Using Federated Variational Autoencoders With Differentially-Private Decodercited by 14
  33. 2021A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensingcited by 13
  34. 2021Efficient Client Contribution Evaluation for Horizontal Federated Learningcited by 12
  35. 2022Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Datacited by 12
  36. 2022Backdoor Attack and Defense in Federated Generative Adversarial Network-based Medical Image Synthesiscited by 12
  37. 2023Synthetic Data: Methods, Use Cases, and Riskscited by 10
  38. 2023Federated Learning with Diffusion Models for Privacy-Sensitive Vision Taskscited by 10

50 most cited of 63 All 63 (JSON)

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Cited by year

Citing papers per year: 2024: 1; 2025: 4.

5 papers.

  1. 2025Attention Eclipse: Manipulating Attention to Bypass LLM Safety-Alignmentcited by 6
  2. 2024Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesiscited by 2
  3. 2025Embedding-Based Federated Data Sharing via Differentially Private Conditional VAEscited by 2
  4. 2025FuSeFL: Fully Secure and Scalable Federated Learningcited by 1
  5. 2025FedDAPL: Toward Client-Private Generalization in Federated Learningcited by 0