LGA

This is the Pytorch Implementation for No One Left Behind: Real-World Federated Class-Incremental Learning

This paper is accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). It is a substantial extension of [CVPR-2022] Federated Class-Incremental Learning

Overview

Requirements:

• python == 3.8
• torch == 1.7.0
• numpy
• PIL
• torchvision == 0.8.1
• cv2
• scipy
• sklearn

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 Mini-Imagenet dataset from the official websites, and place it in './dataset'.

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

Launching an experiment:

For exampler, if you want to run LGA on CIFAR100 in the 10 steps setting:

Modify the path of dataset in './scripts/cifar_task_10.sh' and run the following commands.

sh scripts/cifar_task_10.sh

Citation:

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

@ARTICLE{9616392,
  author={Dong, Jiahua and Li, Hongliu and Cong, Yang and Sun, Gan and Zhang, Yulun and Van Gool, Luc},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)}, 
  title={No One Left Behind: Real-World Federated Class-Incremental Learning}, 
  year={2023}
}

@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},
}

Contact:

If you have some questions, feel free to contact:

• Jiahua Dong: [email protected]