@@ -121,6 +121,7 @@ In PaddleFL, components for defining a federated learning task and training a fe
-**FL-scheduler**: Decide which set of trainers can join the training before each updating cycle.
For more instructions, please refer to the [examples](./python/paddle_fl/paddle_fl/examples)
### Paddle Encrypted
Paddle Fluid Encrypted implements secure training and inference tasks based on the underlying MPC protocol of ABY3[], in which participants can be classified into roles of Input Party (IP), Computing Party (CP) and Result Party (RP).
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@@ -161,6 +162,7 @@ A PFE program is exactly a PaddlePaddle program, and will be executed as normal
Upon completion of the secure training (or inference) job, the models (or prediction results) will be output by CPs in encrypted form. Result Parties can collect the encrypted results, decrypt them using the tools in PFE, and deliver the plaintext results to users.
For more instructions, please refer to [mpc examples](./python/paddle_fl/mpc/examples)
This document introduces how to run MNIST demo based on Paddle-MPC, which has two ways of running, i.e., single machine and multi machines.
###1. Running on Single Machine
###1. Running on Single Machine
####(1). Prepare Data
####(1). Prepare Data
Generate encrypted training and testing data utilizing `generate_encrypted_data()` and `generate_encrypted_test_data()` in `process_data.py` script. For example, users can write the following code into a python script named `prepare.py`, and then run the script with command `python prepare.py`.
Encrypted data files of feature and label would be generated and saved in `/tmp` directory. Different suffix names are used for these files to indicate the ownership of different computation parties. For instance, a file named `mnist2_feature.part0` means it is a feature file of party 0.
####(2). Launch Demo with A Shell Script
####(2). Launch Demo with A Shell Script
Launch demo with the `run_standalone.sh` script. The concrete command is:
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@@ -30,7 +30,7 @@ The information of current epoch and step will be displayed on screen while trai
Besides, predictions would be made in this demo once training is finished. The predictions with cypher text format would be save in `/tmp` directory, and the format of file name is similar to what is described in Step 1.
####(3). Decrypt Data
####(3). Decrypt Data
Decrypt the saved prediction data and save the decrypted prediction results into a specified file using `decrypt_data_to_file()` in `process_data.py` script. For example, users can write the following code into a python script named `decrypt_save.py`, and then run the script with command `python decrypt_save.py`. The decrypted prediction results would be saved into `mpc_label`.
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@@ -51,17 +51,17 @@ fi
###2. Running on Multi Machines
###2. Running on Multi Machines
####(1). Prepare Data
####(1). Prepare Data
Data owner encrypts data. Concrete operations are consistent with “Prepare Data” in “Running on Single Machine”.
####(2). Distribute Encrypted Data
####(2). Distribute Encrypted Data
According to the suffix of file name, distribute encrypted data files to `/tmp ` directories of all 3 computation parties. For example, send `mnist2_feature.part0` and `mnist2_label.part0` to `/tmp` of party 0 with `scp` command.
####(3). Modify mnist_demo.py
####(3). Modify mnist_demo.py
Each computation party modifies `localhost` in the following code as the IP address of it's machine.
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@@ -69,7 +69,7 @@ Each computation party modifies `localhost` in the following code as the IP addr
**Note** that Redis service is necessary for demo running. Remember to clear the cache of Redis server before launching demo on each computation party, in order to avoid any negative influences caused by the cached records in Redis. The following command can be used for clear Redis, where REDIS_BIN is the executable binary of redis-cli, SERVER and PORT represent the IP and port of Redis server respectively.
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@@ -89,7 +89,7 @@ Similarly, predictions with cypher text format would be saved in `/tmp` director
**Note** that remember to delete the precidtion files in `/tmp` directory generated in last running, in case of any influence on the decrypted results of current running.
####(5). Decrypt Prediction Data
####(5). Decrypt Prediction Data
Each computation party sends `mnist_output_prediction.part` file in `/tmp` directory to the `/tmp` directory of data owner. Data owner decrypts the prediction data and saves the decrypted prediction results into a specified file using `decrypt_data_to_file()` in `process_data.py` script. For example, users can write the following code into a python script named `decrypt_save.py`, and then run the script with command `python decrypt_save.py`. The decrypted prediction results would be saved into file `mpc_label`.
This document introduces how to run UCI Housing demo based on Paddle-MPC, which has two ways of running, i.e., single machine and multi machines.
###1. Running on Single Machine
###1. Running on Single Machine
####(1). Prepare Data
####(1). Prepare Data
Generate encrypted data utilizing `generate_encrypted_data()` in `process_data.py` script. For example, users can write the following code into a python script named `prepare.py`, and then run the script with command `python prepare.py`.
Encrypted data files of feature and label would be generated and saved in `/tmp` directory. Different suffix names are used for these files to indicate the ownership of different computation parties. For instance, a file named `house_feature.part0` means it is a feature file of party 0.
####(2). Launch Demo with A Shell Script
####(2). Launch Demo with A Shell Script
Launch demo with the `run_standalone.sh` script. The concrete command is:
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@@ -48,17 +48,17 @@ fi
###2. Running on Multi Machines
###2. Running on Multi Machines
####(1). Prepare Data
####(1). Prepare Data
Data owner encrypts data. Concrete operations are consistent with “Prepare Data” in “Running on Single Machine”.
####(2). Distribute Encrypted Data
####(2). Distribute Encrypted Data
According to the suffix of file name, distribute encrypted data files to `/tmp ` directories of all 3 computation parties. For example, send `house_feature.part0` and `house_label.part0` to `/tmp` of party 0 with `scp` command.
####(3). Modify uci_housing_demo.py
####(3). Modify uci_housing_demo.py
Each computation party makes the following modifications on `uci_housing_demo.py` according to the environment of machine.
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@@ -82,7 +82,7 @@ Each computation party makes the following modifications on `uci_housing_demo.py
**Note** that Redis service is necessary for demo running. Remember to clear the cache of Redis server before launching demo on each computation party, in order to avoid any negative influences caused by the cached records in Redis. The following command can be used for clear Redis, where REDIS_BIN is the executable binary of redis-cli, SERVER and PORT represent the IP and port of Redis server respectively.
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@@ -102,7 +102,7 @@ Similarly, training loss with cypher text format would be printed on the screen
**Note** that remember to delete the loss and precidtion files in `/tmp` directory generated in last running, in case of any influence on the decrypted results of current running.
####(5). Decrypt Loss and Prediction Data
####(5). Decrypt Loss and Prediction Data
Each computation party sends `uci_loss.part` and `uci_prediction.part` files in `/tmp` directory to the `/tmp` directory of data owner. Data owner decrypts and gets the plain text of loss and predictions with ` load_decrypt_data()` in `process_data.py`.
