This repository is built for the paper Federated Disentangled Tuning with Textual Prior Decoupling and Visual Dynamic Adaptation (ICML 25)

• Python 3.8+
• Pytorch 1.10.0+

To install requirements:

pip install -r requirements.txt

You need to manually download and unzip data under data/ file catalog. Remember to set the correct data path via the --root argument when running experiments. The file structure looks like:

data/
 └── Office31/
      ├── amazon/
      ├── dslr/
      └── webcam/

• Office31
• OfficeHome
• PACS
• DomainNet

You can run federated_main.py with specific arguments. After the experiments, all the results are finished and save to output/.

python federated_main.py \
    --trainer FEDDDA \
    --dataset Office31 \
    --device_id 0 \
    OPTIM.MAX_EPOCH 1

For more detailed configuration settings, refer to the configs/ directory and the extended command-line arguments.

Please kindly cite this paper in your publications if it helps your research:

@inproceedings{yang2025FedDDA,
  title={Federated Disentangled Tuning with Textual Prior Decoupling and Visual Dynamic Adaptation},
  author={Yang, Yihao and Huang, Wenke and Wan, Guancheng and Yang, Bin and Ye, Mang},
  booktitle={Forty-second International Conference on Machine Learning},
  year={2025}
}