kpansett / fedgraphnn

A Research-oriented Federated Learning Library and Benchmark Platform for Graph Neural Networks. Accepted to ICLR'2021 - DPML and MLSys'21 - GNNSys workshops.

FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks

A Research-oriented Federated Learning Library and Benchmark Platform for Graph Neural Networks. Accepted to ICLR-DPML and MLSys21 - GNNSys'21 workshops.

Installation

After git clone-ing this repository, please run the following command to install our dependencies.

conda create -n fedgraphnn python=3.8.3
conda activate fedgraphnn
bash install.sh

Data Preparation

FedGraphNN datasets

1. Graph - level
   1. MoleculeNet -> We provide preprocessed versions of MoleculeNet datasets. To use datasets,first run bash download_and_unzip.sh located under each dataset folder in data/moleculenet
   2. Social Networks -> We use PyTorch Geometric datasets for our social network datasets. For details, please see this link
2. Sub-graph Level
   1. Knowledge Graphs -> Please first run bash file inside data/subgraph-level
   2. Recommendation Systems -> We provide preprocessed versions of Ciao and Epinions
3. Node-level
   1. Coaauthor & Citation Networks (Ego Networks) -> Details

Experiments

1. Graph Level
   1. MoleculeNet Centralized Experiments Federated Experiments
   2. Social Networks Federated Experiments
2. Sub-graph Level
   1. Recommendation Systems Federated Experiments
3. Node Level
   1. Ego Networks (Citation & Coauthor Networks) Federated Experiments

How to Add Your Own Model?

Our framework supports PyTorch and PyTorch Geometric based models. To do so,

1. Create a Pytorch/PyG based model and place it under model folder
2. Prepare a trainer module (example) by inheriting the base class in FedML/fedml-core/trainer/fedavg_trainer.py.
3. Prepare an experiment file similar to files in experiments/ folder.

How to Add More Datasets ?

If it is a PyTorch Geometric dataset, please see this link

Otherwise, do the following:

1. Create new folder under data_preprocessing folder and re-define data_preprocessing/data_loader.py based on your new dataset.
2. Rewrite data_loader.py file under data_preprocessing folder

How to Add Domain-Specific Splits & Non-I.I.D.ness Generation Mechanism?

Splits and Non-I.I.D.'ness methods are located under data_preprocessing library. By default, we provide I.I.D. and non-I.I.D. sampling(create_non_uniform_split.py , Dirichlet distribution sampling) based on sample size of the dataset.

To create custom splitting method based on the sample size, you can create a new function or modify create_non_uniform_split.py function.

Code Structure of FedGraphNN

• FedML: A soft repository link generated using git submodule add https://github.com/FedML-AI/FedML.

• data: Provide data downloading scripts and store the downloaded datasets. Note that in FedML/data, there also exists datasets for research, but these datasets are used for evaluating federated optimizers (e.g., FedAvg) and platforms. FedGraphNN supports more advanced datasets and models for federated training of graph neural networks. Currently, we have molecular machine learning datasets.

• data_preprocessing: Domain-specific PyTorch/PyG Data Loaders for centralized and distributed training.

• model: GNN models written in Pytorch/PyG.

• trainer: please define your own trainer.py by inheriting the base class in FedML/fedml-core/trainer/fedavg_trainer.py. Some tasks can share the same trainer.

• experiments/distributed:

1. experiments is the entry point for training. It contains experiments in different platforms. We start from distributed.
2. Every experiment integrates FOUR building blocks FedML (federated optimizers), data_preprocessing, model, trainer.
3. To develop new experiments, please refer the code at experiments/distributed/text-classification.

• experiments/centralized:

1. please provide centralized training script in this directory.
2. This is used to get the reference model accuracy for FL.
3. You may need to accelerate your training through distributed training on multi-GPUs and multi-machines. Please refer the code at experiments/centralized/DDP_demo.

Update FedML Submodule

cd FedML
git checkout master && git pull
cd ..
git add FedML
git commit -m "updating submodule FedML to latest"
git push

Citation

Please cite our FedML and FedGraphNN papers if it helps your research. You can describe us in your paper like this: "We develop our experiments based on FedML".

@misc{he2021fedgraphnn,
      title={FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks}, 
      author={Chaoyang He and Keshav Balasubramanian and Emir Ceyani and Carl Yang and Han Xie and Lichao Sun and Lifang He and Liangwei Yang and Philip S. Yu and Yu Rong and Peilin Zhao and Junzhou Huang and Murali Annavaram and Salman Avestimehr},
      year={2021},
      eprint={2104.07145},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{he2020fedml,
      title={FedML: A Research Library and Benchmark for Federated Machine Learning}, 
      author={Chaoyang He and Songze Li and Jinhyun So and Xiao Zeng and Mi Zhang and Hongyi Wang and Xiaoyang Wang and Praneeth Vepakomma and Abhishek Singh and Hang Qiu and Xinghua Zhu and Jianzong Wang and Li Shen and Peilin Zhao and Yan Kang and Yang Liu and Ramesh Raskar and Qiang Yang and Murali Annavaram and Salman Avestimehr},
      year={2020},
      eprint={2007.13518},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}