mlsanityData to 7 Oct 2026

FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models

arXiv 2407.19953 ↗First posted 29 Jul 2024cs.CVcited by 17

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

Abstract (arXiv)

In recent years, the attention towards One-Shot Federated Learning (OSFL) has been driven by its capacity to minimize communication. With the development of the diffusion model (DM), several methods employ the DM for OSFL, utilizing model parameters, image features, or textual prompts as mediums to transfer the local client knowledge to the server. However, these mediums often require public datasets or the uniform feature extractor, significantly limiting their practicality. In this paper, we propose FedDEO, a Description-Enhanced One-Shot Federated Learning Method with DMs, offering a novel exploration of utilizing the DM in OSFL. The core idea of our method involves training local descriptions on the clients, serving as the medium to transfer the knowledge of the distributed clients to the server. Firstly, we train local descriptions on the client data to capture the characteristics of client distributions, which are then uploaded to the server. On the server, the descriptions are used as conditions to guide the DM in generating synthetic datasets that comply with the distributions of various clients, enabling the training of the aggregated model. Theoretical analyses and sufficient quantitation and visualization experiments on three large-scale real-world datasets demonstrate that through the training of local descriptions, the server is capable of generating synthetic datasets with high quality and diversity. Consequently, with advantages in communication and privacy protection, the aggregated model outperforms compared FL or diffusion-based OSFL methods and, on some clients, outperforms the performance ceiling of centralized training.

Key facts. Cited by 17 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 35 of its 46 references that are in the map. First posted on arXiv 29 Jul 2024.

Builds on

35 of 46 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023One-Shot Heterogeneous Federated Learning with Local Model-Guided Diffusion Models15 Nov
  2. 2023Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Model6 May

Other references · 33

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2020Denoising Diffusion Implicit Modelscited by 9,655
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  5. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  6. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  7. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  8. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
  9. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,231
  10. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  11. 2018Moment Matching for Multi-Source Domain Adaptationcited by 1,327
  12. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
21 more
  1. 2021Variational Diffusion Modelscited by 1,145
  2. 2022Pseudo Numerical Methods for Diffusion Models on Manifoldscited by 695
  3. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
  4. 2021DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulationcited by 640
  5. 2022Compositional Visual Generation with Composable Diffusion Modelscited by 619
  6. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  7. 2021Federated Learning Based on Dynamic Regularizationcited by 555
  8. 2018The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scalecited by 542
  9. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 537
  10. 2023BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusioncited by 280
  11. 2023SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two Secondscited by 227
  12. 2023Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMCcited by 216
  13. 2020Distilled One-Shot Federated Learningcited by 108
  14. 2021DENSE: Data-Free One-Shot Federated Learningcited by 100
  15. 2022NICO++: Towards Better Benchmarking for Domain Generalizationcited by 86
  16. 2023Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generationcited by 54
  17. 2022Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learningcited by 29
  18. 2022Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferringcited by 28
  19. 2023Federated Generative Learning with Foundation Modelscited by 23
  20. 2022One-shot Federated Learning without Server-side Trainingcited by 20
  21. 2023Text-driven Prompt Generation for Vision-Language Models in Federated Learningcited by 16

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 8; 2026: 7.

17 papers.

Sort:

By area:

  1. 2024FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Modelscited by 16
  2. 2025One-Shot Federated Learning with Classifier-Free Diffusion Modelscited by 7
  3. 2025One-shot Federated Learning Methods: A Practical Guidecited by 6
  4. 2025Capture Global Feature Statistics for One-Shot Federated Learningcited by 6
  5. 2024Personalized One-shot Federated Graph Learning for Heterogeneous Clientscited by 3
  6. 2025Global Prompt Refinement with Non-Interfering Attention Masking for One-Shot Federated Learningcited by 2
  7. 2025A New One-Shot Federated Learning Framework for Medical Imaging Classification with Feature-Guided Rectified Flow and Knowledge Distillationcited by 1
  8. 2025FedS2R: One-Shot Federated Domain Generalization for Synthetic-to-Real Semantic Segmentation in Autonomous Drivingcited by 1
  9. 2025FedDEAP: Adaptive Dual-Prompt Tuning for Multi-Domain Federated Learningcited by 1
  10. 2026Distribution Alignment for One-Shot Federated Learning via Optimal Transportcited by 1
  11. 2025WarmFed: Federated Learning with Warm-Start for Globalization and Personalization Via Personalized Diffusion Modelscited by 0
  12. 2026Feature-Aware One-Shot Federated Learning via Hierarchical Token Sequencescited by 0
5 more
  1. 2026Catastrophic Forgetting Resilient One-Shot Incremental Federated Learningcited by 0
  2. 2026FederatedFactory: Generative One-Shot Learning for Extremely Non-IID Distributed Scenarioscited by 0
  3. 2026CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learningcited by 0
  4. 2026Out-of-Distribution Federated Distillation with Domain-Aware Proxycited by 0
  5. 2026Structural Entropy-Driven Graph Diffusion Generation for One-Shot Federated Graph Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025FedDifRC: Unlocking the Potential of Text-to-Image Diffusion Models in Heterogeneous Federated Learning
  2. 2025Federated Learning for Diffusion Models
  3. 2024Navigating Heterogeneity and Privacy in One-Shot Federated Learning with Diffusion Models
  4. 2025FedPhD: Federated Pruning with Hierarchical Learning of Diffusion Models
  5. 2024Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Data
  6. 2022FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers
  7. 2024FedDM: Enhancing Communication Efficiency and Handling Data Heterogeneity in Federated Diffusion Models
  8. 2024FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation