mlsanityData to 7 Oct 2026

FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models

arXiv 2410.04810 ↗First posted 7 Oct 2024cs.LG cs.CV cs.DCcited by 16

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

Abstract (arXiv)

One-Shot Federated Learning (OSFL), a special decentralized machine learning paradigm, has recently gained significant attention. OSFL requires only a single round of client data or model upload, which reduces communication costs and mitigates privacy threats compared to traditional FL. Despite these promising prospects, existing methods face challenges due to client data heterogeneity and limited data quantity when applied to real-world OSFL systems. Recently, Latent Diffusion Models (LDM) have shown remarkable advancements in synthesizing high-quality images through pretraining on large-scale datasets, thereby presenting a potential solution to overcome these issues. However, directly applying pretrained LDM to heterogeneous OSFL results in significant distribution shifts in synthetic data, leading to performance degradation in classification models trained on such data. This issue is particularly pronounced in rare domains, such as medical imaging, which are underrepresented in LDM's pretraining data. To address this challenge, we propose Federated Bi-Level Personalization (FedBiP), which personalizes the pretrained LDM at both instance-level and concept-level. Hereby, FedBiP synthesizes images following the client's local data distribution without compromising the privacy regulations. FedBiP is also the first approach to simultaneously address feature space heterogeneity and client data scarcity in OSFL. Our method is validated through extensive experiments on three OSFL benchmarks with feature space heterogeneity, as well as on challenging medical and satellite image datasets with label heterogeneity. The results demonstrate the effectiveness of FedBiP, which substantially outperforms other OSFL methods.

Key facts. Cited by 16 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 50 of its 58 references that are in the map. First posted on arXiv 7 Oct 2024.

Builds on

50 of 58 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models29 Jul
  2. 2023Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Model6 May

Other references · 48

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  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. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  6. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,069
  7. 2022Generative Adversarial Networkscited by 5,603
  8. 2019Advances and Open Problems in Federated Learningcited by 3,021
  9. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
  10. 2021Learning to Prompt for Vision-Language Modelscited by 2,269
  11. 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,903
  12. 2023AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuningcited by 1,418
36 more
  1. 2018Moment Matching for Multi-Source Domain Adaptationcited by 1,327
  2. 2017Deep Hashing Network for Unsupervised Domain Adaptationcited by 1,181
  3. 2022Imagic: Text-Based Real Image Editing with Diffusion Modelscited by 1,172
  4. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  5. 2022Diffusion Models in Vision: A Surveycited by 1,008
  6. 2017Deeper, Broader and Artier Domain Generalizationcited by 983
  7. 2022Pseudo Numerical Methods for Diffusion Models on Manifoldscited by 695
  8. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 682
  9. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  10. 2018ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Modelscited by 475
  11. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  12. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 380
  13. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 349
  14. 2023Synthetic Data from Diffusion Models Improves ImageNet Classificationcited by 326
  15. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 284
  16. 2020FedBE: Making Bayesian Model Ensemble Applicable to Federated Learningcited by 176
  17. 2020Threats to Federated Learning: A Surveycited by 171
  18. 2022Fake it till you make it: Learning transferable representations from synthetic ImageNet clonescited by 171
  19. 2019One-Shot Federated Learningcited by 124
  20. 2020Distilled One-Shot Federated Learningcited by 108
  21. 2021DENSE: Data-Free One-Shot Federated Learningcited by 100
  22. 2020Practical One-Shot Federated Learning for Cross-Silo Settingcited by 76
  23. 2020XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learningcited by 58
  24. 2022Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environmentscited by 49
  25. 2023Real-Fake: Effective Training Data Synthesis Through Distribution Matchingcited by 35
  26. 2023Prompting Diffusion Representations for Cross-Domain Semantic Segmentationcited by 30
  27. 2022FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stationscited by 28
  28. 2023Generalization by Adaptation: Diffusion-Based Domain Extension for Domain-Generalized Semantic Segmentationcited by 27
  29. 2024Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boostingcited by 27
  30. 2023One-Shot Federated Conformal Predictioncited by 26
  31. 2022Federated Learning with GAN-based Data Synthesis for Non-IID Clientscited by 25
  32. 2022FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentationcited by 24
  33. 2023Federated Generative Learning with Foundation Modelscited by 23
  34. 2022One-shot Federated Learning without Server-side Trainingcited by 20
  35. 2024Parametric Feature Transfer: One-shot Federated Learning with Foundation Modelscited by 13
  36. 2023Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Spacecited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 8; 2026: 8.

16 papers.

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  1. 2025OFFSET: Segmentation-based Focus Shift Revision for Composed Image Retrievalcited by 40
  2. 2025One-shot Federated Learning Methods: A Practical Guidecited by 6
  3. 2026EchoRL: Reinforcement Learning via Rollout Echoingcited by 3
  4. 2026Diffusion-Guided Semantic Consistency for Multimodal Heterogeneitycited by 2
  5. 2025A New One-Shot Federated Learning Framework for Medical Imaging Classification with Feature-Guided Rectified Flow and Knowledge Distillationcited by 1
  6. 2025Edge-Assisted Collaborative Fine-Tuning for Multi-User Personalized Artificial Intelligence Generated Content (AIGC)cited by 1
  7. 2025Conformal Sets in Multiple-Choice Question Answering under Black-Box Settings with Provable Coverage Guaranteescited by 1
  8. 2026Distribution Alignment for One-Shot Federated Learning via Optimal Transportcited by 1
  9. 2025Personalized Federated Training of Diffusion Models with Privacy Guaranteescited by 0
  10. 2025FADE: Adversarial Concept Erasure in Flow Modelscited by 0
  11. 2025Conformal P-Value in Multiple-Choice Question Answering Tasks with Provable Risk Controlcited by 0
  12. 2026One-Shot Crowd Counting With Density Guidance For Scene Adaptationcited by 0
4 more
  1. 2026When Denoising Becomes Unsigning: Theoretical and Empirical Analysis of Watermark Fragility Under Diffusion-Based Image Editingcited by 0
  2. 2026Editing Away the Evidence: Diffusion-Based Image Manipulation and the Failure Modes of Robust Watermarkingcited by 0
  3. 2026UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignmentcited by 0
  4. 2026CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learningcited by 0

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  4. 2025FedDifRC: Unlocking the Potential of Text-to-Image Diffusion Models in Heterogeneous Federated Learning
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  7. 2024FedDM: Enhancing Communication Efficiency and Handling Data Heterogeneity in Federated Diffusion Models
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