This is the official PyTorch implementation of CVPR 2022 paper "RSCFed: Random Sampling Consensus Federated Semi-supervised Learning".
-
Create conda environment:
conda create -n RSCFed python=3.8 conda activate RSCFed -
Install dependencies:
pip install -r requirements.txt
SVHN and CIFAR-100 dataset will be downloaded automatically once training started.
- Train model for each dataset. To produce the claimed results for SVHN dataset:
python train_main.py --dataset=SVHN \
--model=simple-cnn \
--unsup_num=9 \
--batch_size=64 \
--lambda_u=0.02 \
--opt=sgd \
--base_lr=0.03 \
--unsup_lr=0.021 \
--max_grad_norm=5 \
--resume \
--from_labeled \
--rounds=1000 \
--meta_round=3 \
--meta_client_num=5 \
--w_mul_times=6 \
--sup_scale=100 \
--dist_scale=1e4 \
For CIFAR-100 dataset:
python train_main.py --dataset=cifar100 \
--model=simple-cnn \
--unsup_num=9 \
--batch_size=64 \
--lambda_u=0.02 \
--opt=sgd \
--base_lr=0.03 \
--unsup_lr=0.021 \
--max_grad_norm=5 \
--resume \
--from_labeled \
--rounds=1000 \
--meta_round=3 \
--meta_client_num=5 \
--w_mul_times=6 \
--sup_scale=100 \
--dist_scale=1e4 \
For ISIC 2018 dataset, please find the warm-up model here. To produce the claimed result:
python train_main.py --dataset=skin \
--model=resnet18 \
--unsup_num=9 \
--batch_size=12 \
--lambda_u=0.02 \
--opt=sgd \
--base_lr=2e-3 \
--unsup_lr=1e-3 \
--max_grad_norm=5 \
--rounds=800 \
--meta_round=3 \
--meta_client_num=5 \
--w_mul_times=200 \
--pre_sz=250 \
--input_sz=224 \
--dist_scale=0.01 \
--sup_scale=0.01 \
--resume \
--from_labeled \
To produce all the claimed results, please modify the path of warm-up model accordingly. Warm-up models are trained only on labeled clients.
| Parameter | Description |
|---|---|
| dataset | dataset used |
| model | backbone structure |
| unsup_num | number of unlabeled clients |
| batch_size | batch size |
| lambda_u | ratio of loss on unlabeled clients |
| opt | optimizer |
| base_lr | lr on labeled clients |
| unsup_lr | lr on unlabeled clients |
| max_grad_norm | limit maximum gradient |
| resume | resume |
| from_labeled | whether resume from warm-up model |
| rounds | maximum global communication rounds |
| meta_round | number of sub-consensus models |
| meta_client_num | number of clients in each subset |
| w_mul_times | scaling times for labeled clients |
| sup_scale | scaling weights for labeled clients when computing model distance |
| dist_scale | scaling weights when computing model distance |
For SVHN and CIFAR-100 dataset, the best model is placed in final_model. For ISIC2018 dataset, please find the best model here.
Use the following command to generate the claimed results:
python test.py --dataset=SVHN \
--batch_size=5 \
--model=simple-cnn \
For CIFAR-100:
python test.py --dataset=cifar100 \
--batch_size=5 \
--model=simple-cnn \
For ISIC 2018:
python test.py --dataset=skin \
--batch_size=5 \
--model=resnet18 \
--pre_sz=250 \
--input_sz=224 \
For different datasets, please modify file path, arguments "dataset" and "model" correspondingly.
If this code is useful for your research, please consider citing:
@inproceedings{liang2022rscfed,
title={RSCFed: Random Sampling Consensus Federated Semi-supervised Learning},
author={Liang, Xiaoxiao and Lin, Yiqun and Fu, Huazhu and Zhu, Lei and Li, Xiaomeng},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2022}
}
