NU-IDEAS-Lab / FCIL

This is the formal code implementation of the CVPR 2022 paper 'Federated Class Incremental Learning'.

PyTorch Implementation of GLFC

This is the implementation code of CVPR 2022 paper 'Federated Class-Incremental Learning'.

requirement

python == 3.6

torch == 1.2.0

numpy

PIL

torchvision == 0.4.0

cv2

scipy == 1.5.2

sklearn == 0.24.1

pre-preparations

CIFAR100

You don't need to do anything before running the experiments of CIFAR100.

Mini-Imagenet (Imagenet-Subset)

You need to download the Mini-Imagenet from here and place it in './train'.

Tiny-Imagenet

You need to download the Tiny-Imagenet from here and place it in './tiny-imagenet-200'.

run

python fl_main.py

The detailed arguments can be found in './src/option.py'.

performance

CIFAR100

Mini-Imagenet (Imagenet-Subset)

cite

If you find our work is helpful to your research, please consider to cite.

@InProceedings{dong2022federated,
    author = {Jiahua Dong and Lixu Wang and Zhen Fang and Gan Sun and Shichao Xu and Xiao Wang and Qi Zhu},
    title = {Federated Class Incremental Learning},
    booktitle = {CVPR},
    year = {2022}
}