android doc update

android/README.md

@@ -144,11 +144,8 @@ Please follow this tutorial (https://doc.fedml.ai/mlops/user_guide.html) to star
 </tr>
 </table>
 
-
-## About Authors
-
-FedML team starts working on Android Platform from year 2009, the beginning of Android Pixel One smartphone (yeah, we are fans of Google Android). 
-We are good at industrial grade Android development. See CTO and Senior Android Engineer's project experience in Android at https://chaoyanghe.com/industrial-experience/.
+## How to Run?
+https://doc.fedml.ai/cross-device/examples/cross_device_android_example.html
 
 
 ## Want More Advanced APIs or Features?

doc/en/cross-device/examples/cross_device_android_example.md

@@ -1,6 +1,5 @@
 # Federated Learning on Android Smartphones
 
-Please follow this tutorial (https://doc.fedml.ai/mlops/user_guide.html) to start training using FedML BeeHive Platform.
 
 <table>
 <tr>
@@ -16,30 +15,75 @@ Please follow this tutorial (https://doc.fedml.ai/mlops/user_guide.html) to star
 This example will guide you to work through how to run federated learning on Android smartphones.
 The code for this example locates in the following two paths:
 
-
 Android Client (App) and SDK: [https://github.com/FedML-AI/FedML/tree/master/android](https://github.com/FedML-AI/FedML/tree/master/android)
 
+
+
+- App: https://github.com/FedML-AI/FedML/tree/master/android/app
+
+
+- fedmlsdk_demo: https://github.com/FedML-AI/FedML/tree/master/android/fedmlsdk_demo
+
+
+- Android SDK layer (Java API + JNI + So library):
+https://github.com/FedML-AI/FedML/tree/master/android/fedmlsdk
+
+
+- MobileNN: FedML Mobile Training Engine Layer (C++, MNN, PyTorch, etc.): \
+https://github.com/FedML-AI/FedML/tree/master/android/fedmlsdk/MobileNN \
+https://github.com/FedML-AI/MNN \
+https://github.com/FedML-AI/pytorch 
+
 Python Server: [https://github.com/FedML-AI/FedML/tree/master/python/quick_start/beehive](https://github.com/FedML-AI/FedML/tree/master/python/quick_start/beehive)
 
-Android Client is an open source version. Check [this guidance](https://github.com/FedML-AI/FedML/blob/master/android/README.md) to understand the software architecture design. Next show you the step-by-step user experiment of using FedML Beehive.
+The software architecture design for Android App/SDK is illustrated in the figure below.
 
+<img src="./../../_static/image/FedML-Android-Arch.jpg" alt="drawing" style="width:600px;"/>
 
-## **1. Install Synthetic Data on Android Devices**
+Next show you the step-by-step user experiment of using FedML Beehive.
 
-### 1.1 Install the adb command tool on your laptop
 
-If you haven't installed `adb`, please refer to the installation steps at [https://www.xda-developers.com/install-adb-windows-macos-linux/](https://www.xda-developers.com/install-adb-windows-macos-linux/)
+## 1. Setup Environment and Run Android App or Android Demo
 
-Then you should turn on the developer mode and USB debugging options for your Android device. The specific operation of each brand of device is not consistent; you can find and refer to the relevant instructions.
+### 1.1 Android Installation
+- Step 1: Install Android Studio - https://developer.android.com/studio/install
 
-Next please connect the Android device to your laptop, and run the following command to see your device serial number.
 
-```shell
-adb devices
-```
-If it works correctly, it means you have successfully connected your laptop to your mobile device using adb.
+- Step 2: Setup Android SDK \
+https://developer.android.com/about/versions/12/setup-sdk
+https://developer.android.com/studio/releases/platforms (we are using API 32 starting from August 2022)
 
-### 1.2 Transferring data to mobile devices
+
+- Step 3: Setup Android NDK and CMake - https://developer.android.com/studio/projects/install-ndk
+
+### 1.2 Compile and Build
+- Clone customized MNN library (https://github.com/FedML-AI/MNN) into `FedML/android/fedmlsdk/MobileNN/MNN` (shown as below)
+
+<img src="./../../_static/image/MNN_clone_on_MobileNN_folder.png" alt="drawing" style="width:400px;"/>
+
+- Compile and Build Android NDK layer by clicking "Make Project"
+
+<img src="./../../_static/image/android_ndk_build.png" alt="drawing" />
+
+- Start to run if your Android smartphone is connected properly.
+
+<img src="./../../_static/image/launch_android_app.png" alt="drawing" />
+
+## 2. Bind FedML Android App to FedML MLOps Platform
+
+This section guides you through 1) installing Android Apk, 2) binding your Android smartphone devices to FedML MLOps Platform, and 3) set the data path for training.
+
+### 2.1 Connect Android App with FedML MLOps Platform
+After installing FedML Android App (https://github.com/FedML-AI/FedML/tree/master/android/app), please go to the MLOps platform (https://open.fedml.ai) - Beehive and switch to the `Edge Devices` page, you can see a list of **My Edge Devices** at the bottom, as well as a QR code and **Account ID:XXX** at the top right.
+
+<img src="./../../_static/image/beehive-device.png" alt="image-20220427204703095" style="zoom:67%;" />
+
+You can also see the binding devices in **My Edge Devices** list on the web page.
+
+
+## 3 Install Synthetic Data and Set the Data Path
+
+### 3.1 Transferring data to mobile devices
 
 You can download the required data and transfer it to the specified directory of the device with the following command:
 
@@ -72,41 +116,34 @@ adb push $CIFAR10_DIR $ANDROID_DIR
 
 The MNIST dataset folder and the CIFAR-10 dataset folder can be moved to `/sdcard/ai` by running the above script. 
 
-## 2. Get Started with FedML Android App
-
-This section guides you through 1) installing Android Apk, 2) binding your Android smartphone devices to FedML MLOps Platform, and 3) set the data path for training.
-
-After installing FedML Android App (https://github.com/FedML-AI/FedML/tree/master/android/app), please go to the MLOps platform (https://open.fedml.ai) - Beehive and switch to the `Edge Devices` page, you can see a list of **My Edge Devices** at the bottom, as well as a QR code and **Account ID:XXX** at the top right.
-
-<img src="./../../_static/image/beehive-device.png" alt="image-20220427204703095" style="zoom:67%;" />
+### 3.2 Set the Data Path
 
-You can also see the binding devices in the **My Edge Devices** list on the web page.
+<img src="./../../_static/image/android_running.jpeg" alt="image-20220428113930309" style="width:350px;margin:0 auto" />
 
 To set data path on your device, click the top green bar. Set it as the path to the corresponding dataset moved to the Android device (find the folder name starting from ai.fedml).
 
 #### 3. **Deploy FL Server**
 
+- Create an account at FedML MLOps Platform (https://open.fedml.ai)
 
 - Build Python Server Package and Upload to FedML MLOps Platform ("Create Application")
 
 [https://github.com/FedML-AI/FedML/tree/master/python/quick_start/beehive](https://github.com/FedML-AI/FedML/tree/master/python/quick_start/beehive)
-
-- For local debugging of cross-device server, please try 
-
 ```
-sh run_server.sh
+bash build_mlops_pkg.sh
 ```
+You can find the package under `mlops` folder.
 
-- Launch the training by using FedML MLOps (https://open.fedml.ai)
+<img src="./../../_static/image/android-pkg-uploading.png" />
 
-Steps at MLOps: create group -> create project -> create run -> select application (the one we created for Android) -> start run
 
-On the Android side, you will see training status as follows if every step works correctly.
+- Launch the training by using FedML MLOps (https://open.fedml.ai)
 
-<img src="./../../_static/image/android_running.jpeg" alt="image-20220428113930309" style="width:350px;margin:0 auto" />
+Steps at MLOps: create group -> create project -> create run -> select application (the one we uploaded server package for Android) -> start run
 
+On the Android side, you will see training status as follows if every step works correctly.
 
-## 3. Get Started with Integrating Android SDK for Your Host App
+## 4. Get Started with Integrating Android SDK for Your Host App
 
 `android/fedmlsdk_demo` is a short tutorial for integrating Android SDK for your host App.
 
@@ -173,7 +210,7 @@ public class App extends Application {
 }
 ```
 
-## Android SDK APIs 
+## 5. Android SDK APIs 
 At the current stage, we provide high-level APIs with the following three classes.
 
 
@@ -187,7 +224,7 @@ This is the message flow to interact between FedML Android SDK and your host APP
 
 - ai.fedml.edge.request.RequestManager
 
-This is used to to connect your Android SDK with FedML Open Platform (https://open.fedml.ai), which helps you to simplify the deployment, edge collaborative training, experimental tracking, and more.
+This is used to connect your Android SDK with FedML Open Platform (https://open.fedml.ai), which helps you to simplify the deployment, edge collaborative training, experimental tracking, and more.
 
 You can import them in your Java/Android projects as follows. See [android/fedmlsdk_demo/src/main/java/ai/fedml/edgedemo/ui/main/MainFragment.java](fedmlsdk_demo/src/main/java/ai/fedml/edgedemo/ui/main/MainFragment.java) as an example.
 ```
@@ -196,4 +233,9 @@ import ai.fedml.edge.OnTrainProgressListener;
 import ai.fedml.edge.request.RequestManager;
 ```
 
+## Need More Help?
+Join the Community:
+
+Slack: https://join.slack.com/t/fedml/shared_invite/zt-havwx1ee-a1xfOUrATNfc9DFqU~r34w
 
+Discord: https://discord.gg/9xkW8ae6RV