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Model-Contrastive Federated Learning

This is the code for paper Model-Contrastive Federated Learning.

Abstract: Federated learning enables multiple parties to collaboratively train a machine learning model without communicating their local data. A key challenge in federated learning is to handle the heterogeneity of local data distribution across parties. Although many studies have been proposed to address this challenge, we find that they fail to achieve high performance in image datasets with deep learning models. In this paper, we propose MOON: model-contrastive federated learning. MOON is a simple and effective federated learning framework. The key idea of MOON is to utilize the similarity between model representations to correct the local training of individual parties, i.e., conducting contrastive learning in model-level. Our extensive experiments show that MOON significantly outperforms the other state-of-the-art federated learning algorithms on various image classification tasks.


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


Parameter Description
model The model architecture. Options: simple-cnn, resnet50 .
alg The training algorithm. Options: moon, fedavg, fedprox, local_training
dataset Dataset to use. Options: cifar10. cifar100, tinyimagenet
lr Learning rate.
batch-size Batch size.
epochs Number of local epochs.
n_parties Number of parties.
sample_fraction the fraction of parties to be sampled in each round.
comm_round Number of communication rounds.
partition The partition approach. Options: noniid, iid.
beta The concentration parameter of the Dirichlet distribution for non-IID partition.
mu The parameter for MOON and FedProx.
temperature The temperature parameter for MOON.
out_dim The output dimension of the projection head.
datadir The path of the dataset.
logdir The path to store the logs.
device Specify the device to run the program.
seed The initial seed.


Here is an example to run MOON on CIFAR-10 with a simple CNN:

python --dataset=cifar10 \
    --model=simple-cnn \
    --alg=moon \
    --lr=0.01 \
    --mu=5 \
    --epochs=10 \
    --comm_round=100 \
    --n_parties=10 \
    --partition=noniid \
    --beta=0.5 \
    --logdir='./logs/' \
    --datadir='./data/' \


You can download Tiny-ImageNet here. Then, you can follow the instructions to reformat the validation folder.


If you use the same setting as our papers, you can simply adopt the hyperparameters reported in our paper. If you try a setting different from our paper, please tune the hyperparameters of MOON. You may tune mu from {0.001, 0.01, 0.1, 1, 5, 10}. If you have sufficient computing resources, you may also tune temperature from {0.1, 0.5, 1.0} and the output dimension of projection head from {64, 128, 256}.


Please cite our paper if you find this code useful for your research.

      title={Model-Contrastive Federated Learning}, 
      author={Qinbin Li and Bingsheng He and Dawn Song},
      booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},


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