UFDA-Universal-Federated-Domain-Adaptation-with-Practical-Assumptions--accepted by AAAI 2024

This repository provides the implementation for our paper: UFDA: Universal Federated Domain Adaptation with Practical Assumptions

Model Review:

Setup

Install Package Dependencies

We need users to declare a base path to store the dataset as well as the log of the training procedure. The directory structure should be

base_path
│       
└───data
│   │   Office-31
│       │   amazon
│       │   dslr
|       |   webcam
│   │   OfficeHome
│       │   ...
│   │   VisDA2017+ImageCLEF-DA

Our framework now supports four multi-source domain adaptation datasets: Office-Home, Office-31, and VisDA2017+ImageCLEF-DA.

Training
We provide the config files with the format .yaml. To perform the UFDA: Universal Federated Domain Adaptation with Practical Assumptions on the specific dataset (e.g., Office-31), please use the following commands:

python main_new.py --config train-config-office311.yaml --dist-url 'tcp://localhost:13110' --loss_weight 0.01 --loss_penalty 0.00 --prot_start 5

Citation

If you use this code, please cite:

@inproceedings{liu2024ufda,
  title={UFDA: Universal Federated Domain Adaptation with Practical Assumptions},
  author={Liu, Xinhui and Chen, Zhenghao and Zhou, Luping and Xu, Dong and Xi, Wei and Bai, Gairui and Zhao, Yihan and Zhao, Jizhong},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={12},
  pages={14026--14034},
  year={2024}
}