140 lines
3.9 KiB
Markdown
140 lines
3.9 KiB
Markdown
# FS-TFP
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This is the offical repository of **FedDGCN**: A Scalable Federated Learning
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Framework for Traffic Flow Prediction.
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It is also a the traffic flow prediction extension based on [FederatedScope](https://github.com/alibaba/FederatedScope).
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NOTE: This is an early version of **FedDGCN**. The full version will be updated after testing is completed.
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---
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# 1. Environment
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We run the experiment on a **Linux system**, i.e **Ubuntu 22.04**. It has not been tested on other systems yet.
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## Step 1. Create a Conda env
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We recommend using a **Conda** virtual environment. This project supports **Python 3.9** (recommended) and **Python 3.10**.
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**WARNING: Python 3.11 and later versions are not compatible!**
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```
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conda create -n FedDGCN python=3.9
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conda activate FedDGCN
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```
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## Step 2. Install Pytorch
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Download the appropriate version of [PyTorch]( https://pytorch.org/get-started/locally/) based on your device.
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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.
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## Step 3. Install FederatedScope
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git clone this repository, and
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```
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cd FS-TFP
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pip install -e .
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```
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Additionally, you might need to install some extra packages to avoid annoying warnings.
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```
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pip install torch_geometric community rdkit
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```
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# 2. Run the Code
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## Step 1. Prepare the datasets
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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.
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The directory structure of `./data/trafficflow` should be as follows:
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```
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FS-TFP\DATA\TRAFFICFLOW
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├─PeMS03
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├─PeMS04
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├─PeMS07
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└─PeMS08
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```
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## Step 2. Check your Setting
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We have placed the run scripts for the four datasets in the `./scripts/trafficflow_exp_scripts/` directory.
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There are YAML files for four datasets: `{D3, D4, D7, D8}.yaml`.
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You can customize the parameters or use the presets we provide.
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Some key parameters include:
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```
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# Line 3: Adjust the GPU device to use (for multi-GPU machines)
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device: 0
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# Line 8: Adjust the total number of training rounds
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total_round_num: <number_of_rounds>
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# Line 9: Adjust the number of clients based on your machine configuration
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client_num: <number_of_clients>
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# Line 65: Adjust the training loss function
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# Options: L1Loss, RMSE, MAPE
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criterion:
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type: <loss_function>
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```
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**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.
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## Step 3. Run the experiments
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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:
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```
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# PEMSD3
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python federatedscope/main.py --cfg scripts/trafficflow_exp_scripts/D3.yaml
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# PEMSD4
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python federatedscope/main.py --cfg scripts/trafficflow_exp_scripts/D4.yaml
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# PEMSD7
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python federatedscope/main.py --cfg scripts/trafficflow_exp_scripts/D7.yaml
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# PEMSD8
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python federatedscope/main.py --cfg scripts/trafficflow_exp_scripts/D8.yaml
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```
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If you see the following output in your terminal, congratulations! You have successfully run the experiment:
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# 3. Visualize the result
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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.
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```
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python exp/global.py
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```
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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.
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You may install matplotlib first for drawing:
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```
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pip install matplotlib
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```
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# Citation
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TBD |