# FS-TFP
This is the offical repository of **FedDGCN**: A Scalable Federated Learning
Framework for Traffic Flow Prediction.

![overview](./figures/overview.jpg)

It is also a the traffic flow prediction extension based on [FederatedScope](https://github.com/alibaba/FederatedScope).

NOTE: This is an early version of **FedDGCN**. The full version will be updated after testing is completed.

---

# 1. Environment

We run the experiment on a **Linux system**, i.e **Ubuntu 22.04**. It has not been tested on other systems yet.

## Step 1. Create a Conda env

We recommend using a **Conda** virtual environment. This project supports **Python 3.9** (recommended) and **Python 3.10**. 

**WARNING: Python 3.11 and later versions are not compatible!**

```
conda create -n FedDGCN python=3.9
conda activate FedDGCN
```

## Step 2. Install Pytorch

Download the appropriate version of [PyTorch]( https://pytorch.org/get-started/locally/) based on your device.

This project has been tested with **Torch 2.4.0 (recommended)** and **Torch 2.0.0** with **CUDA 12**. Compatibility with other versions is not guaranteed.

## Step 3. Install FederatedScope 

git clone this repository, and

```
cd FS-TFP
pip install -e .
```

Additionally, you might need to install some extra packages to avoid annoying warnings.

```
pip install torch_geometric community rdkit
```



# 2. Run the Code

## Step 1. Prepare the datasets

You need to download the PeMS dataset from the **[STSGCN](https://github.com/Davidham3/STSGCN)** repository following README. After downloading, extract the dataset and place it in the `./data/trafficflow` directory at the root of the project.

The directory structure of `./data/trafficflow` should be as follows:

```
FS-TFP\DATA\TRAFFICFLOW
├─PeMS03
├─PeMS04
├─PeMS07
└─PeMS08
```

## Step 2. Check your Setting

We have placed the run scripts for the four datasets in the `./scripts/trafficflow_exp_scripts/` directory. 

There are YAML files for four datasets: `{D3, D4, D7, D8}.yaml`.

You can customize the parameters or use the presets we provide.

Some key parameters include:

```
# Line 3: Adjust the GPU device to use (for multi-GPU machines)
device: 0  

# Line 8: Adjust the total number of training rounds
total_round_num: <number_of_rounds>

# Line 9: Adjust the number of clients based on your machine configuration
client_num: <number_of_clients>

# Line 65: Adjust the training loss function
# Options: L1Loss, RMSE, MAPE
criterion:
  type: <loss_function>
```

**WARNING:** Processing the **PEMSD7** dataset may require more than **32GB** RAM. If your system lacks sufficient RAM, it is recommended to increase the size of the swap partition.



## Step 3. Run the experiments

You can use the following command to run **FedDGCN** directly. It is recommended to create the corresponding run configuration in your IDE based on the command below:

```
# PEMSD3
python federatedscope/main.py --cfg scripts/trafficflow_exp_scripts/D3.yaml
# PEMSD4
python federatedscope/main.py --cfg scripts/trafficflow_exp_scripts/D4.yaml
# PEMSD7
python federatedscope/main.py --cfg scripts/trafficflow_exp_scripts/D7.yaml
# PEMSD8
python federatedscope/main.py --cfg scripts/trafficflow_exp_scripts/D8.yaml
```



If you see the following output in your terminal, congratulations! You have successfully run the experiment:

![image-20241121193843250](./figures/exp.png)



# 3. Visualize the result

The experiment logs will be placed in the **exp** folder. We have written a script, **global.py**, in the **exp** folder. You need to replace the previous logs with the new ones generated from the experiment. Once replaced, simply run the script to visualize the experiment results.

```
python exp/global.py
```

The script will generate a **baseline.jpg** file to visualize the logs. You are also free to modify the script to implement additional functionality as needed.

You may install matplotlib first for drawing:

```
pip install matplotlib
```



# Citation

TBD