This is partly the reproduction of the paper of Communication-Efficient Learning of Deep Networks from Decentralized Data
Only experiments on MNIST and CIFAR10 (both IID and non-IID) is produced by far.

Note: The scripts will be slow without the implementation of parallel computing.

！！！代码原链接[github]

• data：用来存放相应的数据，可以发现一开始里面是 mnist 和 cifar 的空文件夹。数据会在torchvision package中自动下载
• save：用来存放最后生成的一系列结果，比方说自己定义的 loss 和 accuracy 随通信次数的可视化显示。
• models：里面有Fed.py 用来将模型平均实现 FedAvg、Net.py 定义不同的神经网络类型如 MLP、CNN等、test.py 将最后得到的模型效果拿来测试、update.py 用来定义本地更新的内容.
• utils：里面的options.py用来存放程序的一些超参数的值，方便在程序运行过程中直接调节(不理解的可以在后面看解释)、sampling.py用来对Non-IID的数据分布进行采样的模拟(不懂的可以先放着，后面会解释)。

python>=3.6
pytorch>=0.4

The MLP and CNN models are produced by:

python main_nn.py

Federated learning with MLP and CNN is produced by:

python main_fed.py

See the arguments in options.py.

For example:

python main_fed.py --dataset mnist --iid --num_channels 1 --model cnn --epochs 50 --gpu 0

--all_clients for averaging over all client models

NB: for CIFAR-10, num_channels must be 3.

Results are shown in Table 1 and Table 2, with the parameters C=0.1, B=10, E=5.

Table 1. results of 10 epochs training with the learning rate of 0.01

Table 2. results of 50 epochs training with the learning rate of 0.01

Acknowledgements give to youkaichao.

McMahan, Brendan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Artificial Intelligence and Statistics (AISTATS), 2017.

Shaoxiong Ji. (2018, March 30). A PyTorch Implementation of Federated Learning. Zenodo. http://doi.org/10.5281/zenodo.4321561