Federated Data Model
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Abstract (arXiv)
In artificial intelligence (AI), especially deep learning, data diversity and volume play a pivotal role in model development. However, training a robust deep learning model often faces challenges due to data privacy, regulations, and the difficulty of sharing data between different locations, especially for medical applications. To address this, we developed a method called the Federated Data Model (FDM). This method uses diffusion models to learn the characteristics of data at one site and then creates synthetic data that can be used at another site without sharing the actual data. We tested this approach with a medical image segmentation task, focusing on cardiac magnetic resonance images from different hospitals. Our results show that models trained with this method perform well both on the data they were originally trained on and on data from other sites. This approach offers a promising way to train accurate and privacy-respecting AI models across different locations.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 6 of its 6 references that are in the map. First posted on arXiv 13 Mar 2024.
Builds on
6 of 6 references.
References · 6
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2023GPT-4 Technical Reportcited by 18,132
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
- 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
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Citing papers per year: 2024: 1; 2025: 2.
3 papers.
- 2024MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysiscited by 14
- 2025dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysiscited by 8
- 2025Leveraging Diffusion Model and Image Foundation Model for Improved Correspondence Matching in Coronary Angiographycited by 0
- 2025Leveraging Diffusion Model and Image Foundation Model for Improved Correspondence Matching in Coronary Angiography31 Mar 2025
- 2025dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis13 Mar 2025
- 2024MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis10 May 2024
Similar, not cited
Close in wording, no citation either way
- 2023Federated Learning with Diffusion Models for Privacy-Sensitive Vision Tasks
- 2024Image Distillation for Safe Data Sharing in Histopathology
- 2025Personalized Federated Training of Diffusion Models with Privacy Guarantees
- 2025FedPhD: Federated Pruning with Hierarchical Learning of Diffusion Models
- 2026FedDP-PALD: A Privacy-Preserving Federated Latent Diffusion Framework with Prototype Aggregation for Medical Data Synthesis
- 2024FedDM: Enhancing Communication Efficiency and Handling Data Heterogeneity in Federated Diffusion Models
- 2025Federated Learning for Medical Image Classification: A Comprehensive Benchmark
- 2024FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models