This is the MindSpore implementation of FSMAFL in the following paper.

Wenke Huang, Mang Ye, Xiang Gao, Bo Du. Few-Shot Model Agnostic Federated Learning, in ACM MM, 2022.

FSMAFL(Few-Shot Model Agnostic Federated Learning) is a latent embedding adaptation framework that can address the large domain gap between the public and private datasets in federated learning process. It is based on two parts:

1. Latent embedding adaptation confuses domain classifier to reduce the impact of domain gap.

2. Model agnostic federated learning is responsible for public-private communication. The public dataset acts as the bridge for model communication and private dataset is used for avoiding forgetting.

Our experiments are conducted on two datasets, MNIST and EMNIST-Letter. The public dataset on the server is set to MNIST. and the private dataset on the client is set to EMNIST-Letter.

Note: Data will be processed in data_init.py

Hardware

• Support Ascend environment.
• Ascend: Ascend 910.

Framework

• MindSpore

For more information, please check the resources below：

• MindSpore Tutorials
• MindSpore Python API

After installing MindSpore via the official website, you can start training and evaluation as follows:

# Initialize the local models
python model_initialization.py
# FSMAFL
python Communication_GAN.py

├── FSMAFL
    ├── Dataset
        ├── MNIST
            ├── test
                ├── t10k-images-idx3-ubyte
                ├── t10k-labels-idx1-ubyte
            ├── train
                ├── train-images-idx3-ubyte
                ├── train-labels-idx1-ubyte
        ├── emnist-letters
    ├── Temp
        ├── priv_data_72.npy
        ├── total_priv_data_72.pickle
    ├── collabporate_train.py
    ├── Communication_GAN.py
    ├── data_utils.py
    ├── model_initialization.py
    ├── model_utils.py
    ├── models.py
    ├── option.py
    ├── README.md

The experimental setting is slightly different from the original paper due to different platforms. In the heterogeneous model scenario, we assign five different networks in models.py. The accuracy(%) is based on the EMNIST-Letter dataset. Initial represents only do initialization without federated learning process.

@inproceedings{huang2022fewshot,
  title={Few-Shot Model Agnostic Federated Learning},
  author={Huang, Wenke and Ye, Mang and Gao, Xiang and Du, Bo},
  booktitle={ACM MM Industry Track},
  year={2022}
}