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README.md
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......@@ -80,6 +80,7 @@ Besides, PFM is implemented based on secure multi-party computation (MPC) to ena
Below, we will introduce them into details:
### Data Parallel
<img src='images/FL-training.png' width = "1000" height = "400" align="middle"/>
In PaddleFL, components for defining a federated learning task and training a federated learning job are as follows:
......@@ -106,66 +107,15 @@ For more instructions, please refer to the [examples](./python/paddle_fl/paddle_
### Federated Learning with MPC
## Easy deployment with kubernetes
### Data Parallel
```sh
kubectl apply -f ./python/paddle_fl/paddle_fl/examples/k8s_deployment/master.yaml
```
Please refer [K8S deployment example](./python/paddle_fl/paddle_fl/examples/k8s_deployment/README.md) for details
You can also refer [K8S cluster application and kubectl installation](./python/paddle_fl/paddle_fl/examples/k8s_deployment/deploy_instruction.md) to deploy your K8S cluster
### Federated Learning with MPC
To be added.
## Benchmark task
### Horzontal Federated Learning
### Data Parallel
Gru4Rec [9] introduces recurrent neural network model in session-based recommendation. PaddlePaddle's Gru4Rec implementation is in https://github.com/PaddlePaddle/models/tree/develop/PaddleRec/gru4rec. An example is given in [Gru4Rec in Federated Learning](https://paddlefl.readthedocs.io/en/latest/examples/gru4rec_examples.html)
### Federated Learning with MPC
#### A. Convergence of paddle_fl.mpc vs paddle
##### 1. Training Parameters
- Dataset: Boston house price dataset
- Number of Epoch: 20
- Batch Size: 10
##### 2. Experiment Results
| Epoch/Step | paddle_fl.mpc | Paddle |
| ---------- | ------------- | ------ |
| Epoch=0, Step=0 | 738.39491 | 738.46204 |
| Epoch=1, Step=0 | 630.68834 | 629.9071 |
| Epoch=2, Step=0 | 539.54683 | 538.1757 |
| Epoch=3, Step=0 | 462.41159 | 460.64722 |
| Epoch=4, Step=0 | 397.11516 | 395.11017 |
| Epoch=5, Step=0 | 341.83102 | 339.69815 |
| Epoch=6, Step=0 | 295.01114 | 292.83597 |
| Epoch=7, Step=0 | 255.35141 | 253.19429 |
| Epoch=8, Step=0 | 221.74739 | 219.65132 |
| Epoch=9, Step=0 | 193.26459 | 191.25981 |
| Epoch=10, Step=0 | 169.11423 | 167.2204 |
| Epoch=11, Step=0 | 148.63138 | 146.85835 |
| Epoch=12, Step=0 | 131.25081 | 129.60391 |
| Epoch=13, Step=0 | 116.49708 | 114.97599 |
| Epoch=14, Step=0 | 103.96669 | 102.56854 |
| Epoch=15, Step=0 | 93.31706 | 92.03858 |
| Epoch=16, Step=0 | 84.26219 | 83.09653 |
| Epoch=17, Step=0 | 76.55664 | 75.49785 |
| Epoch=18, Step=0 | 69.99673 | 69.03561 |
| Epoch=19, Step=0 | 64.40562 | 63.53539 |
We conduct tests on PFM using Boston house price dataset, and the implementation is given in [uci_demo](./python/paddle_fl/mpc/examples/uci_demo)
## On Going and Future Work
......
README_cn.md
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......@@ -188,39 +188,7 @@ kubectl apply -f ./python/paddle_fl/paddle_fl/examples/k8s_deployment/master.yam
Gru4Rec [9] 在基于会话的推荐中引入了递归神经网络模型。PaddlePaddle的GRU4RC实现代码在 https://github.com/PaddlePaddle/models/tree/develop/PaddleRec/gru4rec. 一个基于联邦学习训练Gru4Rec模型的示例请参考[Gru4Rec in Federated Learning](https://paddlefl.readthedocs.io/en/latest/examples/gru4rec_examples.html)
### Federated Learning with MPC
#### A. 精度测试
##### 1. 训练参数
- 数据集：波士顿房价预测。
- 训练轮数： 20
- Batch Size：10
##### 2. 实验结果
| Epoch/Step | paddle_fl.mpc | Paddle |
| ---------- | ------------- | ------ |
| Epoch=0, Step=0 | 738.39491 | 738.46204 |
| Epoch=1, Step=0 | 630.68834 | 629.9071 |
| Epoch=2, Step=0 | 539.54683 | 538.1757 |
| Epoch=3, Step=0 | 462.41159 | 460.64722 |
| Epoch=4, Step=0 | 397.11516 | 395.11017 |
| Epoch=5, Step=0 | 341.83102 | 339.69815 |
| Epoch=6, Step=0 | 295.01114 | 292.83597 |
| Epoch=7, Step=0 | 255.35141 | 253.19429 |
| Epoch=8, Step=0 | 221.74739 | 219.65132 |
| Epoch=9, Step=0 | 193.26459 | 191.25981 |
| Epoch=10, Step=0 | 169.11423 | 167.2204 |
| Epoch=11, Step=0 | 148.63138 | 146.85835 |
| Epoch=12, Step=0 | 131.25081 | 129.60391 |
| Epoch=13, Step=0 | 116.49708 | 114.97599 |
| Epoch=14, Step=0 | 103.96669 | 102.56854 |
| Epoch=15, Step=0 | 93.31706 | 92.03858 |
| Epoch=16, Step=0 | 84.26219 | 83.09653 |
| Epoch=17, Step=0 | 76.55664 | 75.49785 |
| Epoch=18, Step=0 | 69.99673 | 69.03561 |
| Epoch=19, Step=0 | 64.40562 | 63.53539 |
我们基于波士顿房价数据集对PFM进行了测试，具体的事例及实现请参考 [uci_demo](./python/paddle_fl/mpc/examples/uci_demo)
## 正在进行的工作
......
docs/source/md/compile_and_install.md 0 → 100644
浏览文件 @ 33ee28e8
## Compile From Source Code
#### A. Environment preparation
* CentOS 6 or CentOS 7 (64 bit)
* Python 2.7.15+/3.5.1+/3.6/3.7 ( 64 bit) or above
* pip or pip3 9.0.1+ (64 bit)
* PaddlePaddle release 1.8
* Redis 5.0.8 (64 bit)
* GCC or G++ 4.8.3+
* cmake 3.15+
#### B. Clone the source code, compile and install
Fetch the source code and checkout stable release
```sh
git clone https://github.com/PaddlePaddle/PaddleFL
cd /path/to/PaddleFL
# Checkout stable release
mkdir build && cd build
```
Execute compile commands, where `PYTHON_EXECUTABLE` is path to the python binary where the PaddlePaddle is installed, `CMAKE_CXX_COMPILER` is the path of G++ and `PYTHON_INCLUDE_DIRS` is the corresponding python include directory. You can get the `PYTHON_INCLUDE_DIRS` via the following command:
```sh
${PYTHON_EXECUTABLE} -c "from distutils.sysconfig import get_python_inc;print(get_python_inc())"
```
Then you can put the directory in the following command and make:
```sh
cmake ../ -DPYTHON_EXECUTABLE=${PYTHON_EXECUTABLE} -DPYTHON_INCLUDE_DIRS=${python_include_dir} -DCMAKE_CXX_COMPILER=${g++_path}
make -j$(nproc)
```
Install the package:
```sh
make install
cd /path/to/PaddleFL/python
${PYTHON_EXECUTABLE} setup.py sdist bdist_wheel
pip or pip3 install dist/***.whl -U
```
python/paddle_fl/mpc/examples/uci_demo/README.md
浏览文件 @ 33ee28e8
......@@ -122,3 +122,39 @@ print("prediction:")
process_data.load_decrypt_data("/tmp/uci_prediction", (BATCH_SIZE,))
```
#### Convergence of paddle_fl.mpc vs paddle
Below, is the result of our experiment to test the convergence of paddle_fl.mpc
#### A. Convergence of paddle_fl.mpc vs paddle
##### 1. Training Parameters
- Dataset: Boston house price dataset
- Number of Epoch: 20
- Batch Size: 10
##### 2. Experiment Results
| Epoch/Step | paddle_fl.mpc | Paddle |
| ---------- | ------------- | ------ |
| Epoch=0, Step=0 | 738.39491 | 738.46204 |
| Epoch=1, Step=0 | 630.68834 | 629.9071 |
| Epoch=2, Step=0 | 539.54683 | 538.1757 |
| Epoch=3, Step=0 | 462.41159 | 460.64722 |
| Epoch=4, Step=0 | 397.11516 | 395.11017 |
| Epoch=5, Step=0 | 341.83102 | 339.69815 |
| Epoch=6, Step=0 | 295.01114 | 292.83597 |
| Epoch=7, Step=0 | 255.35141 | 253.19429 |
| Epoch=8, Step=0 | 221.74739 | 219.65132 |
| Epoch=9, Step=0 | 193.26459 | 191.25981 |
| Epoch=10, Step=0 | 169.11423 | 167.2204 |
| Epoch=11, Step=0 | 148.63138 | 146.85835 |
| Epoch=12, Step=0 | 131.25081 | 129.60391 |
| Epoch=13, Step=0 | 116.49708 | 114.97599 |
| Epoch=14, Step=0 | 103.96669 | 102.56854 |
| Epoch=15, Step=0 | 93.31706 | 92.03858 |
| Epoch=16, Step=0 | 84.26219 | 83.09653 |
| Epoch=17, Step=0 | 76.55664 | 75.49785 |
| Epoch=18, Step=0 | 69.99673 | 69.03561 |
| Epoch=19, Step=0 | 64.40562 | 63.53539 |
python/paddle_fl/mpc/examples/uci_demo/README_CN.md
浏览文件 @ 33ee28e8
......@@ -124,3 +124,36 @@ print("prediction:")
process_data.load_decrypt_data("/tmp/uci_prediction", (BATCH_SIZE,))
```
#### A. 精度测试
##### 1. 训练参数
- 数据集：波士顿房价预测。
- 训练轮数： 20
- Batch Size：10
##### 2. 实验结果
| Epoch/Step | paddle_fl.mpc | Paddle |
| ---------- | ------------- | ------ |
| Epoch=0, Step=0 | 738.39491 | 738.46204 |
| Epoch=1, Step=0 | 630.68834 | 629.9071 |
| Epoch=2, Step=0 | 539.54683 | 538.1757 |
| Epoch=3, Step=0 | 462.41159 | 460.64722 |
| Epoch=4, Step=0 | 397.11516 | 395.11017 |
| Epoch=5, Step=0 | 341.83102 | 339.69815 |
| Epoch=6, Step=0 | 295.01114 | 292.83597 |
| Epoch=7, Step=0 | 255.35141 | 253.19429 |
| Epoch=8, Step=0 | 221.74739 | 219.65132 |
| Epoch=9, Step=0 | 193.26459 | 191.25981 |
| Epoch=10, Step=0 | 169.11423 | 167.2204 |
| Epoch=11, Step=0 | 148.63138 | 146.85835 |
| Epoch=12, Step=0 | 131.25081 | 129.60391 |
| Epoch=13, Step=0 | 116.49708 | 114.97599 |
| Epoch=14, Step=0 | 103.96669 | 102.56854 |
| Epoch=15, Step=0 | 93.31706 | 92.03858 |
| Epoch=16, Step=0 | 84.26219 | 83.09653 |
| Epoch=17, Step=0 | 76.55664 | 75.49785 |
| Epoch=18, Step=0 | 69.99673 | 69.03561 |
| Epoch=19, Step=0 | 64.40562 | 63.53539 |
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