Co-MDA: Federated Multi-source Domain Adaptation on Black-box Models

This repository provides the implementation for our paper: Co-MDA: Federated Multi-source Domain Adaptation on Black-box Models

Model Review:

Setup

Install Package Dependencies

Python Environment == 3.7 
torch == 1.3.1
torchvision == 0.4.2
tensorboard == 2.4.1
tensorboardX == 2.0
numpy
yaml

Install Datasets

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

base_path
│       
└───dataset
│   │   DigitFive
│       │   mnist_data.mat
│       │   mnistm_with_label.mat
|       |   svhn_train_32x32.mat  
│       │   ...
│   │   DomainNet
│       │   ...
│   │   OfficeCaltech10
│       │   ...
|   |   OfficeHome

Our framework now support four multi-source domain adaptation datasets: DigitFive, DomainNet, OfficeCaltech10 and OfficeHome.

Dataset Preparation

DigitFive: The DigitFive dataset can be accessed in DigitFive.

OfficeHome: The OfficeCaltech10 dataset can be accessed in OfficeHome.

DomainNet: The DomainNet dataset can be accessed in DomainNet.

Training
The configuration files can be found under the folder ./config, and we provide four config files with the format .yaml. To perform the Federated Multi-source Domain Adaptation on Black-Box Models on the specific dataset (e.g., DigitFive), please use the following commands:

python main.py --config DigitFive.yaml --target-domain mnistm -bp base_path -forget_rate 0.4
python main.py --config DigitFive.yaml --target-domain mnist -bp base_path -forget_rate 0.04
python main.py --config DigitFive.yaml --target-domain svhn -bp base_path -forget_rate 0.08

Citation

If you use this code, please cite:

@article{liu2023co,
  title={Co-MDA: Federated Multi-source Domain Adaptation on Black-box Models},
  author={Liu, Xinhui and Xi, Wei and Li, Wen and Xu, Dong and Bai, Gairui and Zhao, Jizhong},
  journal={IEEE Transactions on Circuits and Systems for Video Technology},
  year={2023},
  publisher={IEEE}
}