/FCIL

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

Primary LanguagePython

Official Pytorch Implementation for GLFC

[CVPR-2022] Federated Class-Incremental Learning

This is the official implementation code of our paper "Federated Class-Incremental Learning" accepted by CVPR-2022.

You can also find the arXiv version with supplementary material at here.

Framework:

overview

Prerequisites:

  • python == 3.6

  • torch == 1.2.0

  • numpy

  • PIL

  • torchvision == 0.4.0

  • cv2

  • scipy == 1.5.2

  • sklearn == 0.24.1

Datasets:

  • CIFAR100: You don't need to do anything before running the experiments on CIFAR100 dataset.

  • Imagenet-Subset (Mini-Imagenet): Please manually download the on Imagenet-Subset (Mini-Imagenet) dataset from the official websites, and place it in './train'.

  • Tiny-Imagenet: Please manually download the on Tiny-Imagenet dataset from the official websites, and place it in './tiny-imagenet-200'.

Training:

  • Please check the detailed arguments in './src/option.py'.
python fl_main.py

Performance:

  • Experiments on CIFAR100 dataset

cifar

  • Experiments on Imagenet-Subset (Mini-Imagenet) dataset

imagenet-subset

Citation:

If you find this code is useful to your research, please consider to cite our paper.

@InProceedings{dong2022federated,
    author = {Dong, Jiahua and Wang, Lixu and Fang, Zhen and Sun, Gan and Xu, Shichao and Wang, Xiao and Zhu, Qi},
    title = {Federated Class-Incremental Learning},
    booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {June},
    year = {2022},
}

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