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

This is the official code repository for the CVPR 2025 paper titled "FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models". Please do not hesitate to reach out for any questions.

Installation Instructions

This code has been verified with python 3.9 and CUDA version 11.7. To get started, navigate to the InterpretDiffusion directory and install the necessary packages using the following commands:

git clone git@github.com:HaokunChen245/FedBiP.git
cd FedBiP
pip install -r requirements.txt
pip install -e diffusers

Data Preparation

Download the datasets and put them in the /data der

• DomainNet: https://ai.bu.edu/M3SDA/
• PACS: https://huggingface.co/datasets/flwrlabs/pacs
• UCM: https://huggingface.co/datasets/blanchon/UC_Merced
• OfficeHome: https://huggingface.co/datasets/flwrlabs/office-home

Training

• Concept-level personalization

bash train.sh

• Image Generation

bash generate.sh

• Classification Model Training

bash clf_train.sh

Citing our work

If our work has contributed to your research, we would greatly appreciate an acknowledgement by citing us as follows:

@article{chen2024fedbip,
  title={Fedbip: Heterogeneous one-shot federated learning with personalized latent diffusion models},
  author={Chen, Haokun and Li, Hang and Zhang, Yao and Bi, Jinhe and Zhang, Gengyuan and Zhang, Yueqi and Torr, Philip and Gu, Jindong and Krompass, Denis and Tresp, Volker},
  journal={arXiv preprint arXiv:2410.04810},
  year={2024}
}