mlsanityData to 7 Oct 2026

Navigating Heterogeneity and Privacy in One-Shot Federated Learning with Diffusion Models

arXiv 2405.01494 ↗First posted 2 May 2024cs.CV cs.CR cs.LGcited by 7

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

Abstract (arXiv)

Federated learning (FL) enables multiple clients to train models collectively while preserving data privacy. However, FL faces challenges in terms of communication cost and data heterogeneity. One-shot federated learning has emerged as a solution by reducing communication rounds, improving efficiency, and providing better security against eavesdropping attacks. Nevertheless, data heterogeneity remains a significant challenge, impacting performance. This work explores the effectiveness of diffusion models in one-shot FL, demonstrating their applicability in addressing data heterogeneity and improving FL performance. Additionally, we investigate the utility of our diffusion model approach, FedDiff, compared to other one-shot FL methods under differential privacy (DP). Furthermore, to improve generated sample quality under DP settings, we propose a pragmatic Fourier Magnitude Filtering (FMF) method, enhancing the effectiveness of generated data for global model training.

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 23 of its 39 references that are in the map. First posted on arXiv 2 May 2024.

Builds on

23 of 39 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Extracting Training Data from Diffusion Models30 Jan

Other references · 22

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  4. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  5. 2019Advances and Open Problems in Federated Learningcited by 3,021
  6. 2016Deep Learning with Differential Privacycited by 2,682
  7. 2021Model-Contrastive Federated Learningcited by 762
  8. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  9. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 682
  10. 2021Federated Learning Based on Dynamic Regularizationcited by 555
  11. 2021Tackling the Generative Learning Trilemma with Denoising Diffusion GANscited by 515
  12. 2021Opacus: User-Friendly Differential Privacy Library in PyTorchcited by 375
10 more
  1. 2021See through Gradients: Image Batch Recovery via GradInversioncited by 320
  2. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  3. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 284
  4. 2019SCAFFOLD: Stochastic Controlled Averaging for Federated Learningcited by 174
  5. 2019One-Shot Federated Learningcited by 124
  6. 2021Local Learning Matters: Rethinking Data Heterogeneity in Federated Learningcited by 114
  7. 2020Distilled One-Shot Federated Learningcited by 108
  8. 2021DENSE: Data-Free One-Shot Federated Learningcited by 100
  9. 2020Mitigating Bias in Federated Learningcited by 56
  10. 2019One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Themcited by 23

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 2.

7 papers.

  1. 2025One-Shot Federated Learning with Classifier-Free Diffusion Modelscited by 7
  2. 2024CollaFuse: Collaborative Diffusion Modelscited by 4
  3. 2025FedBM: Stealing Knowledge from Pre-trained Language Models for Heterogeneous Federated Learningcited by 2
  4. 2025Towards One-shot Federated Learning: Advances, Challenges, and Future Directionscited by 2
  5. 2025Personalized Federated Training of Diffusion Models with Privacy Guaranteescited by 0
  6. 2026Catastrophic Forgetting Resilient One-Shot Incremental Federated Learningcited by 0
  7. 2026FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuningcited 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. 2025FedDifRC: Unlocking the Potential of Text-to-Image Diffusion Models in Heterogeneous Federated Learning
  4. 2023Federated Learning with Diffusion Models for Privacy-Sensitive Vision Tasks
  5. 2024Disentangling data distribution for Federated Learning
  6. 2025FedPref: Federated Learning Across Heterogeneous Multi-objective Preferences
  7. 2024Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis
  8. 2024FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models