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+# Federated Learning
+
+This is partly the reproduction of the paper of [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://arxiv.org/abs/1602.05629)   
+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. 
+
+## Run
+
+The MLP and CNN models are produced by:
+> python [main_nn.py](main_nn.py)
+
+The testing accuracy of MLP on MINST: 92.14% (10 epochs training) with the learning rate of 0.01.
+The testing accuracy of CNN on MINST: 98.37% (10 epochs training) with the learning rate of 0.01.
+
+Federated learning with MLP and CNN is produced by:
+> python [main_fed.py](main_fed.py)
+
+See the arguments in [options.py](utils/options.py). 
+
+For example:
+> python main_fed.py --dataset mnist --num_channels 1 --model cnn --epochs 50 --gpu 0 
+
+
+## Results
+### MNIST
+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
+
+| Model     | Acc. of IID | Acc. of Non-IID|
+| -----     | -----       | ----           |
+| FedAVG-MLP|  85.66%     | 72.08%         |
+| FedAVG-CNN|  95.00%     | 74.92%         |
+
+Table 2. results of 50 epochs training with the learning rate of 0.01
+
+| Model     | Acc. of IID | Acc. of Non-IID|
+| -----     | -----       | ----           |
+| FedAVG-MLP| 84.42%      | 88.17%         |
+| FedAVG-CNN| 98.17%      | 89.92%         |
+
+
+## Requirements
+python 3.6
+
+pytorch 0.4
+# LG-FedAvg
+# LG-FedAvg