mlsanityData to 14 Oct 2026

A New One-Shot Federated Learning Framework for Medical Imaging Classification with Feature-Guided Rectified Flow and Knowledge Distillation

arXiv 2507.19045 ↗First posted 25 Jul 2025cs.CV cs.DCcited by 1

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

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Abstract (arXiv)

In multi-center scenarios, One-Shot Federated Learning (OSFL) has attracted increasing attention due to its low communication overhead, requiring only a single round of transmission. However, existing generative model-based OSFL methods suffer from low training efficiency and potential privacy leakage in the healthcare domain. Additionally, achieving convergence within a single round of model aggregation is challenging under non-Independent and Identically Distributed (non-IID) data. To address these challenges, in this paper a modified OSFL framework is proposed, in which a new Feature-Guided Rectified Flow Model (FG-RF) and Dual-Layer Knowledge Distillation (DLKD) aggregation method are developed. FG-RF on the client side accelerates generative modeling in medical imaging scenarios while preserving privacy by synthesizing feature-level images rather than pixel-level images. To handle non-IID distributions, DLKD enables the global student model to simultaneously mimic the output logits and align the intermediate-layer features of client-side teacher models during aggregation. Experimental results on three non-IID medical imaging datasets show that our new framework and method outperform multi-round federated learning approaches, achieving up to 21.73% improvement, and exceeds the baseline FedISCA by an average of 21.75%. Furthermore, our experiments demonstrate that feature-level synthetic images significantly reduce privacy leakage risks compared to pixel-level synthetic images. The code is available at https://github.com/LMIAPC/one-shot-fl-medical.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models. Builds on 28 of its 43 references that are in the map. First posted on arXiv 25 Jul 2025.

Builds on

28 of 43 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025One-Shot Federated Learning with Classifier-Free Diffusion Models12 Feb
  2. 2024FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models7 Oct
  3. 2024FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models29 Jul
  4. 2023Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Model6 May
  5. 2023Extracting Training Data from Diffusion Models30 Jan

Other references · 23

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  5. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  6. 2022Scalable Diffusion Models with Transformerscited by 6,054
  7. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,700
  8. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  9. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  10. 2021Model-Contrastive Federated Learningcited by 766
  11. 2019One-Shot Federated Learningcited by 125
  12. 2020Distilled One-Shot Federated Learningcited by 109
11 more
  1. 2021DENSE: Data-Free One-Shot Federated Learningcited by 100
  2. 2020Practical One-Shot Federated Learning for Cross-Silo Settingcited by 77
  3. 2017Nonlinear Information Bottleneckcited by 71
  4. 2024Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boostingcited by 27
  5. 2023Federated Generative Learning with Foundation Modelscited by 23
  6. 2024FedFMS: Exploring Federated Foundation Models for Medical Image Segmentationcited by 21
  7. 2024Parametric Feature Transfer: One-shot Federated Learning with Foundation Modelscited by 13
  8. 2021FedSLD: Federated Learning with Shared Label Distribution for Medical Image Classificationcited by 10
  9. 2025Federated Learning for Medical Image Classification: A Comprehensive Benchmarkcited by 6
  10. 2025MedDiff-FT: Data-Efficient Diffusion Model Fine-tuning with Structural Guidance for Controllable Medical Image Synthesiscited by 5
  11. 2024MPCPA: Multi-Center Privacy Computing with Predictions Aggregation based on Denoising Diffusion Probabilistic Modelcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Efficient 3D Whole-Body PET Image Denoising via Conditional Rectified Flow With Optimized Sampling Strategycited by 0

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Close in wording, no citation either way

  1. 2025FedSKD: Aggregation-free Model-heterogeneous Federated Learning via Multi-dimensional Similarity Knowledge Distillation for Medical Image Classification
  2. 2026FedBiCross: Personalized One-Shot Federated Learning on Medical Images
  3. 2026Feature-Aware One-Shot Federated Learning via Hierarchical Token Sequences
  4. 2023Dealing With Heterogeneous 3D MR Knee Images: A Federated Few-Shot Learning Method With Dual Knowledge Distillation
  5. 2023Federated Learning for Data and Model Heterogeneity in Medical Imaging
  6. 2022Federated Learning with Privacy-Preserving Ensemble Attention Distillation
  7. 2026FederatedFactory: Generative One-Shot Learning for Extremely Non-IID Distributed Scenarios
  8. 2026DSFedMed: Dual-Scale Federated Medical Image Segmentation via Mutual Distillation Between Foundation and Lightweight Models