mlsanityData to 7 Oct 2026

One-Shot Federated Learning with Classifier-Free Diffusion Models

arXiv 2502.08488 ↗First posted 12 Feb 2025cs.LGcited by 7

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

Abstract (arXiv)

Federated learning (FL) enables collaborative learning without data centralization but introduces significant communication costs due to multiple communication rounds between clients and the server. One-shot federated learning (OSFL) addresses this by forming a global model with a single communication round, often relying on the server's model distillation or auxiliary dataset generation - mostly through pre-trained diffusion models (DMs). Existing DM-assisted OSFL methods, however, typically employ classifier-guided DMs, which require training auxiliary classifier models at each client, introducing additional computation overhead. This work introduces OSCAR (One-Shot Federated Learning with Classifier-Free Diffusion Models), a novel OSFL approach that eliminates the need for auxiliary models. OSCAR uses foundation models to devise category-specific data representations at each client which are integrated into a classifier-free diffusion model pipeline for server-side data generation. In our experiments, OSCAR outperforms the state-of-the-art on four benchmark datasets while reducing the communication load by at least 99%.

Key facts. Cited by 7 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 33 of its 40 references that are in the map. First posted on arXiv 12 Feb 2025.

Builds on

33 of 40 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models29 Jul
  2. 2024Navigating Heterogeneity and Privacy in One-Shot Federated Learning with Diffusion Models2 May
  3. 2023Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Model6 May

Other references · 30

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  5. 2023GPT-4 Technical Reportcited by 18,132
  6. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  7. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  8. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  9. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,069
  10. 2022Classifier-Free Diffusion Guidancecited by 5,717
  11. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,310
  12. 2019Advances and Open Problems in Federated Learningcited by 3,021
18 more
  1. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  2. 2018Moment Matching for Multi-Source Domain Adaptationcited by 1,327
  3. 2021Federated Learning Based on Dynamic Regularizationcited by 555
  4. 2018The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scalecited by 542
  5. 2022Federated Learning from Pre-Trained Models: A Contrastive Learning Approachcited by 141
  6. 2022PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models -- Federated Learning in Age of Foundation Modelcited by 133
  7. 2019One-Shot Federated Learningcited by 124
  8. 2020Distilled One-Shot Federated Learningcited by 108
  9. 2021DENSE: Data-Free One-Shot Federated Learningcited by 100
  10. 2022NICO++: Towards Better Benchmarking for Domain Generalizationcited by 86
  11. 2020Practical One-Shot Federated Learning for Cross-Silo Settingcited by 76
  12. 2023FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learningcited by 59
  13. 2023Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Modelscited by 49
  14. 2023Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learningcited by 29
  15. 2023Federated Generative Learning with Foundation Modelscited by 23
  16. 2023Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updatescited by 20
  17. 2023Greedy Shapley Client Selection for Communication-Efficient Federated Learningcited by 6
  18. 2024Spatial-Temporal Attention Model for Traffic State Estimation with Sparse Internet of Vehiclescited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 2.

7 papers.

  1. 2025MTF-Grasp: A Multi-tier Federated Learning Approach for Robotic Graspingcited by 1
  2. 2025A New One-Shot Federated Learning Framework for Medical Imaging Classification with Feature-Guided Rectified Flow and Knowledge Distillationcited by 1
  3. 2025Federated Learning for Large-Scale Cloud Robotic Manipulation: Opportunities and Challengescited by 0
  4. 2025FedMP: Tackling Medical Feature Heterogeneity in Federated Learning from a Manifold Perspectivecited by 0
  5. 2025Edge AI in Highly Volatile Environments: Is Fairness Worth the Accuracy Trade-off?cited by 0
  6. 2026Catastrophic Forgetting Resilient One-Shot Incremental Federated Learningcited by 0
  7. 2026FederatedFactory: Generative One-Shot Learning for Extremely Non-IID Distributed Scenarioscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023One-Shot Heterogeneous Federated Learning with Local Model-Guided Diffusion Models
  2. 2024FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models
  3. 2025Federated Learning for Diffusion Models
  4. 2025FedDifRC: Unlocking the Potential of Text-to-Image Diffusion Models in Heterogeneous Federated Learning
  5. 2024Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Data
  6. 2025FedPhD: Federated Pruning with Hierarchical Learning of Diffusion Models
  7. 2022FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers
  8. 2024Training Diffusion Models with Federated Learning