mlsanityData to 7 Oct 2026

Phoenix: A Federated Generative Diffusion Model

arXiv 2306.04098 ↗First posted 7 Jun 2023cs.LG cs.CVcited by 21

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

Abstract (arXiv)

Generative AI has made impressive strides in enabling users to create diverse and realistic visual content such as images, videos, and audio. However, training generative models on large centralized datasets can pose challenges in terms of data privacy, security, and accessibility. Federated learning (FL) is an approach that uses decentralized techniques to collaboratively train a shared deep learning model while retaining the training data on individual edge devices to preserve data privacy. This paper proposes a novel method for training a Denoising Diffusion Probabilistic Model (DDPM) across multiple data sources using FL techniques. Diffusion models, a newly emerging generative model, show promising results in achieving superior quality images than Generative Adversarial Networks (GANs). Our proposed method Phoenix is an unconditional diffusion model that leverages strategies to improve the data diversity of generated samples even when trained on data with statistical heterogeneity or Non-IID (Non-Independent and Identically Distributed) data. We demonstrate how our approach outperforms the default diffusion model in an FL setting. These results indicate that high-quality samples can be generated by maintaining data diversity, preserving privacy, and reducing communication between data sources, offering exciting new possibilities in the field of generative AI.

Key facts. Cited by 21 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 31 of its 52 references that are in the map. First posted on arXiv 7 Jun 2023.

Builds on

31 of 52 references.

References · 31

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  4. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  5. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  6. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  7. 2022Generative Adversarial Networkscited by 5,603
  8. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  9. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  10. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  11. 2018Federated Learning with Non-IID Datacited by 1,099
  12. 2020Adaptive Federated Optimizationcited by 1,036
19 more
  1. 2019Improved Precision and Recall Metric for Assessing Generative Modelscited by 925
  2. 2021Score-based Generative Modeling in Latent Spacecited by 634
  3. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  4. 2019Federated Learning with Personalization Layerscited by 533
  5. 2022Fast Sampling of Diffusion Models with Exponential Integratorcited by 526
  6. 2021Tackling the Generative Learning Trilemma with Denoising Diffusion GANscited by 515
  7. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  8. 2018Assessing Generative Models via Precision and Recallcited by 401
  9. 2019Fair Resource Allocation in Federated Learningcited by 389
  10. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  11. 2021Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Modelscited by 251
  12. 2020LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasetscited by 60
  13. 2023Speed Is All You Need: On-Device Acceleration of Large Diffusion Models via GPU-Aware Optimizationscited by 45
  14. 2022Federated Learning with GAN-based Data Synthesis for Non-IID Clientscited by 25
  15. 2020Federated Generative Adversarial Learningcited by 19
  16. 2020A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspectivecited by 14
  17. 2022PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networkscited by 14
  18. 2021Training Federated GANs with Theoretical Guarantees: A Universal Aggregation Approachcited by 8
  19. 2020Wireless for Machine Learningcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 9; 2025: 7; 2026: 3.

21 papers.

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  1. 2024Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learningcited by 23
  2. 2024Deep Generative Models for 3D Medical Image Synthesiscited by 14
  3. 2023A Survey of Emerging Applications of Diffusion Probabilistic Models in MRIcited by 9
  4. 2024FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generationcited by 7
  5. 2025Federated Learning for Diffusion Modelscited by 6
  6. 2023One-Shot Heterogeneous Federated Learning with Local Model-Guided Diffusion Modelscited by 5
  7. 2024FedDM: Enhancing Communication Efficiency and Handling Data Heterogeneity in Federated Diffusion Modelscited by 5
  8. 2024CollaFuse: Collaborative Diffusion Modelscited by 4
  9. 2025FedDifRC: Unlocking the Potential of Text-to-Image Diffusion Models in Heterogeneous Federated Learningcited by 3
  10. 2024On-demand Quantization for Green Federated Generative Diffusion in Mobile Edge Networkscited by 2
  11. 2024Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesiscited by 2
  12. 2025Fed-NDIF: A Noise-Embedded Federated Diffusion Model For Low-Count Whole-Body PET Denoisingcited by 1
9 more
  1. 2025FedPhD: Federated Pruning with Hierarchical Learning of Diffusion Modelscited by 1
  2. 2025Bridging Generalization Gap of Heterogeneous Federated Clients Using Generative Modelscited by 1
  3. 2024CollaFuse: Navigating Limited Resources and Privacy in Collaborative Generative AIcited by 0
  4. 2024CCVA-FL: Cross-Client Variations Adaptive Federated Learning for Medical Imagingcited by 0
  5. 2025Personalized Federated Training of Diffusion Models with Privacy Guaranteescited by 0
  6. 2025Cooperative Perception: A Resource-Efficient Framework for Multi-Drone 3D Scene Reconstruction Using Federated Diffusion and NeRFcited by 0
  7. 2026Compositional Generative Modeling from Decentralized Datacited by 0
  8. 2026FedOT: Ownership Verification and Leakage Tracing via Watermarks for Federated LDMscited by 0
  9. 2026Generative AI and Federated Learning for Intrusion Detection Systems: A Surveycited by 0

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  1. 2024Training Diffusion Models with Federated Learning
  2. 2024Generative AI in Vision: A Survey on Models, Metrics and Applications
  3. 2024Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memory
  4. 2023Federated Learning with Diffusion Models for Privacy-Sensitive Vision Tasks
  5. 2022Towards Practical Plug-and-Play Diffusion Models
  6. 2025Ambient Diffusion Omni: Training Good Models with Bad Data
  7. 2024Enhancing Diffusion Models for High-Quality Image Generation
  8. 2023Extracting Training Data from Diffusion Models