docs: 📚 update README

instruction for how to create an ethereum account and metaMask wallet

.gitignore

@@ -4,6 +4,7 @@ dataset
 
 *.png
 
+!**/figures/**
 
 models
 

README.md

@@ -1,5 +1,25 @@
 # Decentralized Neural Network Using Blockchain
 
+- [Federated learning + blockchain + ipfs](#federated-learning--blockchain--ipfs)
+    - [set up](#1-set-up)
+    - [account preparation](#2-account-preparation)
+    - [dataset preparation](#3-dataset-preparation)
+    - [run training nodes](#4-run-the-training-nodes)
+- [Simulation on different applications (models)](#simulation-on-different-applications-models)
+    - [deploy new model](#1-deploy-model)
+    - [store model hash](#2-add-the-model-hash-to-blockchain)
+    - [define data handling](#3-change-the-datahandlerpy)
+    - [model optimization](#4-change-the-trainpy)
+    - [basic config set up](#5-change-the-configpy)
+- [accuracy simulation](#accuracy-simulation)
+- [Federated learning + central server](#federated-learning--central-server)
+    - [set up](#1-set-up-1)
+    - [run an ipfs node locally](#2-run-ipfs-node)
+    - [run the central server](#3-run-a-server-after-running-the-ipfs-daemon)
+    - [run training nodes](#4-run-the-training-nodes-1)
+- [downloading training script](#server-for-downloading-the-training-scripts)
+    - [set up](#1-set-up-2)
+    - [run the server](#2-run-the-server)
 ## Federated learning + blockchain + ipfs
 
 ### **1. Set up**
@@ -14,9 +34,9 @@ pip install -r requirements.txt
 
 ### **2. Account Preparation:**
 
-Since you need to provide the private key to make the transiction, you need to create a file called **accounts.py** at /src/training-scripts/py/[modelName]/ and add your account info
+Since you need to provide the private key to make the transiction, you need to create a file called **accounts.py** at `/src/training-scripts/py/[modelName]/` and add your account info
 
-e.g. /src/training-scripts/py/cifar10/accounts.py
+e.g. `/src/training-scripts/py/cifar10/accounts.py`
 
 ```python
 # this is for single account simulation without the incentive mechanism
@@ -39,11 +59,30 @@ accounts = [
 ]
 ```
 
+> you need to at least have an account for ropsten testnet;
+
+ **to quickly create an ethereum account, you can use `metaMask` as a `wallet` to create accounts and manage all your accounts**
+
+1. go to [chrome web store](https://chrome.google.com/webstore/detail/metamask/nkbihfbeogaeaoehlefnkodbefgpgknn) to install the metaMask plugin.
+2. set up your wallet following the instruction and keep your wallet private key words safe.
+3. after creating a wallet, you can now create an account.
+
+![](./figures/create_account.PNG)
+
+4. after your account has been created, you can see your `account address` and import the `private key` from metaMask. Besides, one account can be used in `different ethereum networks` including `ropsten testnet`, you can switch to different networks in metaMask as well(the ETH can not be shared through different networks)
+
+![](./figures/account_detail.PNG)
+
+5. then you can go to [Ropsten Testnet Explorer](https://ropsten.etherscan.io/) to get info of your account
+6. lastly, you may want some ETH for the simulation, you can find a [ropsten faucet](https://faucet.egorfine.com/) to request some ETH. There should be a few different faucets. you can also ask me to give you some.
+
+> The reason why you need to provide the `account private key` is that we use the [Infura](https://infura.io/dashboard) as a `Provider` to connect to the blockchain. `Provider` can be considered as one of the nodes of a blockchain network. When we do a `state-mutated transaction`, the `Provider` need our account address with private key to execute the transaction.
+> To use the `Infura`, we need to apply a key for each project, currently is using mine, you can change it [here](https://github.com/s-elo/DNN-Blockchain/blob/33a142e431bc5710b8ecdaea2224df94bd9e0727/src/training-scripts/py/cifar10/contract.py#L11)
 ### **3. Dataset Preparation:**
 
 **\*This is not necessary any more, since the data will be loaded using tensorflow API for quick simulation**
 
-Put the training dataset at /src/training-scripts/py/modelName/dataset. Use the names of the classes as the sub-folder name
+Put the training dataset at `/src/training-scripts/py/modelName/dataset`. Use the names of the classes as the sub-folder name
 
 ### **4. Run the training nodes:**
 
@@ -68,14 +107,18 @@ python main.py
 
 - The limited number of the nodes and training rounds can be set by modifying the two variables **NODE_NUM** and **ROUND** in the **config.py**.
 
+> Note that we have deployed a `contract` and `model` for simulation. 
+> The `contract info` you can find from [here](https://ropsten.etherscan.io/address/0xecc03bcae3944ff618787c209d64f8f5cfee1456#code) or at the `/src/training-scripts/py/cirfar10/contract.json`. The code is at [UI branch](https://github.com/s-elo/DNN-Blockchain/blob/contractsAndUI/contracts/PureDnn.sol).
+> The model is deployed to the ipfs through [WebStorage](https://web3.storage/account/)
+> Upload files to ipfs through WebStorage needs a `apiToken`, currently is using mine. you can change it [here](https://github.com/s-elo/DNN-Blockchain/blob/33a142e431bc5710b8ecdaea2224df94bd9e0727/src/ipfs_deploy/deploy.ts#L13)
 ## Simulation on different applications (models)
 
-If you want to try simulation on differnt models, you can do some changes based on the cifar10 training scripts **/src/training-scripts/py/cirfar10**
+If you want to try simulation on differnt models, you can do some changes based on the cifar10 training scripts `/src/training-scripts/py/cirfar10`
 
 ### **1. Deploy model**
 
 Firstly we need to deploy the new model **(only for tensorflow models)** on the ipfs.
-at /src/ipfs_deploy/ create a new model folder just like **/src/ipfs_deploy/cifar10** which is able to generate the model.json file corresponding to the new model.
+at `/src/ipfs_deploy/` create a new model folder just like `/src/ipfs_deploy/cifar10` which is able to generate the model.json file corresponding to the new model.
 
 After getting the model.json file:
 
@@ -85,26 +128,26 @@ npm run deploy [new model name(the folder name)]
 
 ### **2. Add the model hash to blockchain**
 
-After deployment, we should be able to get the hash of the model at /src/ipfs_deploy/deploy_hash.json (the last one), then you need to add this hash to the blockchain by updating the smart contract state
+After deployment, we should be able to get the hash of the model at `/src/ipfs_deploy/deploy_hash.json` (the last one), then you need to add this hash to the blockchain by updating the smart contract state
 using the explorer [here](https://ropsten.etherscan.io/address/0xecc03bcae3944ff618787c209d64f8f5cfee1456#writeContract). We should be able to use the **addNewModel** function and add the hash with the model name. The testset_hash can just be any string since we dont get the testset from ipfs for simulation.
 
 ### **3. Change the dataHandler.py**
 
-After the above deployment, you need to customize how to get your training dataset as what the original cifar10 model does at **/src/training-scripts/py/cirfar10/dataHandler.py**. You can choose to load the dataset using tf API or download the dataset manually.
+After the above deployment, you need to customize how to get your training dataset as what the original cifar10 model does at `/src/training-scripts/py/cirfar10/dataHandler.py`. You can choose to load the dataset using tf API or download the dataset manually.
 
 ### **4. Change the train.py**
 
-The train.py at **/src/training-scripts/py/cirfar10/train.py** determines how to compile and optimize the fetched model from ipfs and model evalation which can be also customized.
+The train.py at `/src/training-scripts/py/cirfar10/train.py` determines how to compile and optimize the fetched model from ipfs and model evalation which can be also customized.
 
 ### **5. Change the config.py**
 
-The config.py at **/src/training-scripts/py/cirfar10/config.py** is used to set some simulation parameters, at this point, the must-do change is the MODEL_NAME param to the name of your new deployed model and feel free to try other different params.
+The config.py at `/src/training-scripts/py/cirfar10/config.py` is used to set some simulation parameters, at this point, the must-do change is the MODEL_NAME param to the name of your new deployed model and feel free to try other different params.
 
 After that, you should be able to do the simulation using the new application.
 
 ## Accuracy simulation
 
-At the /acc_simul, you can simulate the model using normal training and federated learning
+At the `/acc_simul`, you can simulate the model using normal training and federated learning
 
 ```bash
 # at /acc_simul/[model name]
@@ -129,7 +172,7 @@ python fl_simul.py
 pip install -r requirements.txt
 ```
 
-Everything is the same as that for **Build the network using Blockchain** except:
+Everything is the same as that for **Building the network using Blockchain** except:
 
 ### **2. Run IPFS node:**
 
@@ -157,7 +200,7 @@ python main.py <port> <data set>
 
 ## Server for Downloading the Training Scripts
 
-### Set up
+### **1. Set up**
 
 **Requirement:** nodeJS v16.13.1; npm 8.1.2
 
@@ -166,13 +209,14 @@ python main.py <port> <data set>
 npm install
 ```
 
-### Run the server
+### **2. Run the server**
 
 ```bash
 # at the root /, install all the dependencies
 npm run start
 ```
 
 Then you can download the training scripts via http://localhost:3500/get-scripts/(modelName)-(scriptType)
-
 e.g. http://localhost:3500/get-scripts/cifar10-py
+
+> this is meant to be used as an api for the [UI](https://github.com/s-elo/DNN-Blockchain/tree/contractsAndUI) to download the script