Official Pytorch Implementation for TagFed

This is the implementation code of the CVPR 2024 paper "Traceable Federated Continual Learning"

Framework:

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 main.py

Performance:

Acknowledge:

We would like to thank the following related open source works:

1. [CVPR-2022] Federated Class-Incremental Learning [Code]
2. [NeurIPS-2019] Compacting, Picking and Growing for Unforgetting Continual Learning Code