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

Related tags

Deep Learning incremental-learning federated-learning continual-learning cvpr2022
Overview

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},
}

Contact:

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Comments
  • Incremental classes in clients

    Incremental classes in clients

    I was wondering that in the paper when you mention that you add the same number of classes in the clients to perform incremental in classes, the new classes added in the same client, are they of the same category? For e.g. suppose I have a model that predicts dog breeds. Suppose client 1 receives data on breed x and incremental learning is performed with this new data for breed and client 2 received data on breed y, and similarly incremental learning is performed for breed y, so are breed x= breed y or can they even be different breeds?

    opened by rishabhpahuja 5
Owner
Race Wang
I am now a second-year Computer Science Ph.D. student at Northwestern University.
Race Wang
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