FedGELA

[NeurIPS 2023]Federated Learning with Bilateral Curation for Partially Class-Disjoint Data

Dependencies

• PyTorch >= 1.0.0
• torchvision >= 0.2.1
• scikit-learn >= 0.23.1

Data Preparing

Here we provide the implementation on SVHN, Cifar-10 and Cifar100 datasets. The three datasets will be automatically downloaded in your datadir.

Model Structure

As for model used in the paper, we use the same model structure ResNet18 modified for 32x32 input as MOON.

Parameters

Parameter	Description
proxy	Type of last layer of classifier you used (cls for FedAvg and etf for our FedGELA).
model	The model architecture. Options: simple-cnn, resnet18.
dataset	Dataset to use. Options: CIFAR10. CIFAR100, SVHN
lr	Learning rate.
batch-size	Batch size.
epochs	Number of local epochs.
n_parties	Number of parties.
party_per_round	number of active clients in each round.
comm_round	Number of communication rounds.
beta	The concentration parameter of the Dirichlet distribution for non-IID partition. Setting 100000 as IID
datadir	The path of the dataset.
logdir	The path to store the logs.
seed	The initial seed.
temperature	Temperature in MOON and Ew in our paper.
mu	Param of baselines.

Usage

Here is an example to run FedGELA on CIFAR10 with ResNet18:

python FedGELA.py --dataset=cifar10 \
    --partition='dirichlet' \
    --temperature=0.00001 \
    --lr=0.01 \
    --epochs=10 \
    --model=resnet18 \
    --comm_round=100 \
    --n_parties=50 \
    --beta=0.5 \
    --party_per_round=10 \
    --logdir='./logs/' \
    --datadir='./data/' \
    --proxy='etf' \

Acknowledgement

We borrow some codes from MOON and FedSkip.

Contact

If you have any problem with this code, please feel free to contact [email protected] or [email protected].

Cite

If you find it useful, please cite as following:

@inproceedings{fan2023federated,
  title={Federated Learning with Bilateral Curation for Partially Class-Disjoint Data},
  author={Fan, Ziqing and Zhang, Ruipeng and Yao, Jiangchao and Han, Bo and Zhang, Ya and Wang, Yanfeng},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
  year={2023}
}