On-Device Deployment

The following describes how to deploy the Federated-Client in the Android and x86 environments:

Android

Building a Package

Configure the build environment.

Currently, only the Linux build environment is supported. For details about how to configure the Linux build environment, click here.
Turn on Federated-Client compile option and build the AAR package that contains aarch64 and aarch32 in the mindspore home directory.
```
export MSLITE_ENABLE_FL=on
bash build.sh -A on -j32
```
Store the generated AAR package in the following path:
```
mindspore-lite-maven-{version}.zip
```

Running Dependencies

Android Studio >= 4.0
Android SDK >= 29

Building a Dependency Environment

After the mindspore-lite-maven-{version}.zip file is decompressed, the following directory structure is obtained:

mindspore-lite-maven-{version}
└── mindspore
    └── mindspore-lite
        └── {version}
            └── mindspore-lite-{version}.aar  # MindSpore Lite training framework AAR package

Therefore, the AAR package path related to federated learning is as follows:

mindspore/output/mindspore/mindspore-lite/{version}/mindspore-lite-{version}.aar

The directory structure related to federated learning in the AAR package is as follows:

mindspore-lite-{version}
├── jni
│   ├── arm64-v8a
│   │   ├── libjpeg.so   # Dynamic library file for image processing
│   │   ├── libminddata-lite.so  # Dynamic library file for image processing
│   │   ├── libmindspore-lite.so  # Dynamic library on which the MindSpore Lite inference framework depends
│   │   ├── libmindspore-lite-jni.so  # JNI dynamic library on which the MindSpore Lite inference framework depends
│   │   ├── libmindspore-lite-train.so  # Dynamic library on which the MindSpore Lite training framework depends
│   │   ├── libmindspore-lite-train-jni.so  # JNI dynamic library on which the MindSpore Lite training framework depends
│   │   └── libturbojpeg.so  # Dynamic library file for image processing
│   └── armeabi-v7a
 │       ├── libjpeg.so   # Dynamic library file for image processing
│       ├── libminddata-lite.so  # Dynamic library file for image processing
│       ├── libmindspore-lite.so  # Dynamic library on which the MindSpore Lite inference framework depends
│       ├── libmindspore-lite-jni.so  # JNI dynamic library on which the MindSpore Lite inference framework depends
│       ├── libmindspore-lite-train.so  # Dynamic library on which the MindSpore Lite training framework depends
│       ├── libmindspore-lite-train-jni.so  # JNI dynamic library on which the MindSpore Lite training framework depends
│       └── libturbojpeg.so  # Dynamic library file for image processing
├── libs
│   ├── mindspore-lite-java-common.jar  # MindSpore Lite training framework JAR package
│   └── mindspore-lite-java-flclient.jar  # Federated learning framework JAR package
└── classes.jar  # MindSpore Lite training framework JAR package

Note 1: since the federated learning jar package in the Android environment does not contain the dependent third-party open source software packages, in the Android environment, before using the AAR package, the user needs to add related dependency statements in the dependencies{} field to load the three open source software that Federated Learning depends on, and the dependencies{} field is in the app/build.gradle file under the Android project, as shown below:

dependencies {

// Add third-party open source software that federated learning relies on
implementation group: 'com.squareup.okhttp3', name: 'okhttp', version: '3.14.9'
implementation group: 'com.google.flatbuffers', name: 'flatbuffers-java', version: '2.0.0'
implementation(group: 'org.bouncycastle',name: 'bcprov-jdk15on', version: '1.68')
}

For specific implementation, please refer to the example of app/build.gradle provided in the Android project configuration dependencies section in the document sentiment classification application.

Note 2: since the third-party open source software bcprov-jdk15on that Federated Learning relies on contains multi-version class files, in order to prevent errors in compiling high-version class files with lower version jdk, the following setting statement can be added to the gradle.properties file of the Android project:

android.jetifier.blacklist=bcprov

After setting up the dependencies shown above in the Android project, you only need to rely on the AAR package to call APIs provided by federated learning. For details about how to call and run the APIs, see the API description of federated learning.

x86

Building a Package

Configure the build environment.

Currently, only the Linux build environment is supported. For details about how to configure the Linux build environment, click here.
Build the x86-related architecture package in the mindspore home directory.
```
bash build.sh -I x86_64 -j32
```
Store the generated x86 architecture package in the following path:
```
mindspore/output/mindspore-lite-{version}-linux-x64.tar.gz
```

Running Dependencies

Python >= 3.7.5
OpenJDK >= 1.9

Building a Dependency Environment

After the mindspore/output/mindspore-lite-{version}-linux-x64.tar.gz file is decompressed, the following directory structure is obtained:

mindspore-lite-{version}-linux-x64
├── tools
│   ├── benchmark_train # Tool for commissioning the performance and accuracy of the training model
│   ├── converter       # Model conversion tool
│   └── cropper         # Library cropping tool
│       ├── cropper                 # Executable file of the library cropping tool
│       └── cropper_mapping_cpu.cfg # Configuration file required for cropping the CPU library
└── runtime
    ├── include  # Header file of the training framework
    │   └── registry # Header file for custom operator registration
    ├── lib      # Training framework library
    │   ├── libminddata-lite.a          # Static library file for image processing
    │   ├── libminddata-lite.so        # Dynamic library file for image processing
    │   ├── libmindspore-lite-jni.so   # JNI dynamic library on which the MindSpore Lite inference framework depends
    │   ├── libmindspore-lite-train.a  # Static library on which the MindSpore Lite training framework depends
    │   ├── libmindspore-lite-train.so # Dynamic library on which the MindSpore Lite training framework depends
    │   ├── libmindspore-lite-train-jni.so # JNI dynamic library on which the MindSpore Lite training framework depends
    │   ├── libmindspore-lite.a  # Static library on which the MindSpore Lite inference framework depends
    │   ├── libmindspore-lite.so  # Dynamic library on which the MindSpore Lite inference framework depends
    │   ├── mindspore-lite-java.jar    # MindSpore Lite training framework JAR package
    │   └── mindspore-lite-java-flclient.jar  # Federated learning framework JAR package
    └── third_party
        └── libjpeg-turbo
            └── lib
                ├── libjpeg.so.62   # Dynamic library file for image processing
                └── libturbojpeg.so.0  # Dynamic library file for image processing

The x86 packages required for federated learning are as follows:

libjpeg.so.62   # Dynamic library file for image processing
libminddata-lite.so  # Dynamic library file for image processing
libmindspore-lite.so  # Dynamic library on which the MindSpore Lite inference framework depends
libmindspore-lite-jni.so  # JNI dynamic library on which the MindSpore Lite inference framework depends
libmindspore-lite-train.so  # Dynamic library on which the MindSpore Lite training framework depends
libmindspore-lite-train-jni.so # JNI dynamic library on which the MindSpore Lite training framework depends
libturbojpeg.so.0  # Dynamic library file for image processing
mindspore-lite-java-flclient.jar  # Federated learning framework JAR package

Find the seven .so files on which federated learning depends in the directories mindspore/output/mindspore-lite-{version}-linux-x64/runtime/lib/ and mindspore/output/mindspore-lite-{version}-linux-x64/runtime/third_party/libjpeg-turbo/lib. Then, place these .so files in a folder, for example, /resource/x86libs/.

Set environment variables in the x86 system (an absolute path must be provided):

export LD_LIBRARY_PATH=/resource/x86libs/:$LD_LIBRARY_PATH

After the dependency environment is set, you can simulate the startup of multiple clients in the x86 environment for federated learning. For details, click here.