FedScale is a scalable and extensible open-source federated learning (FL) engine and benchmark.
FedScale (fedscale.ai) provides high-level APIs to implement FL algorithms, deploy and evaluate them at scale across diverse hardware and software backends. FedScale also includes the largest FL benchmark that contains FL tasks ranging from image classification and object detection to language modeling and speech recognition. Moreover, it provides datasets to faithfully emulate FL training environments where FL will realistically be deployed.
You can simply run install.sh.
source install.sh # Add `--cuda` if you want CUDA
pip install -e .
Update install.sh if you prefer different versions of conda/CUDA.
If you have Anaconda installed and cloned FedScale, here are the instructions.
cd FedScale
# Please replace ~/.bashrc with ~/.bash_profile for MacOS
FEDSCALE_HOME=$(pwd)
echo export FEDSCALE_HOME=$(pwd) >> ~/.bashrc
echo alias fedscale=\'bash $FEDSCALE_HOME/fedscale.sh\' >> ~/.bashrc
conda init bash
. ~/.bashrc
conda env create -f environment.yml
conda activate fedscale
pip install -e .
Finally, install NVIDIA CUDA 10.2 or above if you want to use FedScale with GPU support.
Now that you have FedScale installed, you can start exploring FedScale following one of these introductory tutorials.
- Explore FedScale datasets
- Deploy your FL experiment
- Implement an FL algorithm
- Deploy FL on smartphones
We are adding more datasets! Please contribute!
FedScale consists of 20+ large-scale, heterogeneous FL datasets and 70+ various models, covering computer vision (CV), natural language processing (NLP), and miscellaneous tasks.
Each one is associated with its training, validation, and testing datasets.
We acknowledge the contributors of these raw datasets. Please go to the ./benchmark/dataset directory and follow the dataset README for more details.
FedScale Runtime is an scalable and extensible deployment as well as evaluation platform to simplify and standardize FL experimental setup and model evaluation. It evolved from our prior system, Oort, which has been shown to scale well and can emulate FL training of thousands of clients in each round.
Please go to ./fedscale/cloud directory and follow the README to set up FL training scripts and the README for practical on-device deployment.
Repo Root
|---- fedscale # FedScale source code
|---- cloud # Core of FedScale service
|---- utils # Auxiliaries (e.g, model zoo and FL optimizer)
|---- edge # Backends for practical deployments (e.g., mobile)
|---- dataloaders # Data loaders of benchmarking dataset
|---- docker # FedScale docker and container deployment (e.g., Kubernetes)
|---- benchmark # FedScale datasets and configs
|---- dataset # Benchmarking datasets
|---- configs # Example configurations
|---- scripts # Scripts for installing dependencies
|---- examples # Examples of implementing new FL designs
|---- docs # FedScale tutorials and APIs
Please read and/or cite as appropriate to use FedScale code or data or learn more about FedScale.
@inproceedings{fedscale-icml22,
title={{FedScale}: Benchmarking Model and System Performance of Federated Learning at Scale},
author={Fan Lai and Yinwei Dai and Sanjay S. Singapuram and Jiachen Liu and Xiangfeng Zhu and Harsha V. Madhyastha and Mosharaf Chowdhury},
booktitle={International Conference on Machine Learning (ICML)},
year={2022}
}and
@inproceedings{oort-osdi21,
title={Oort: Efficient Federated Learning via Guided Participant Selection},
author={Fan Lai and Xiangfeng Zhu and Harsha V. Madhyastha and Mosharaf Chowdhury},
booktitle={USENIX Symposium on Operating Systems Design and Implementation (OSDI)},
year={2021}
}Please submit issues or pull requests as you find bugs or improve FedScale.
For each submission, please add unit tests to the corresponding changes and make sure that all unit tests pass by running pytest fedscale/tests.
If you have any questions or comments, please join our Slack channel, or email us ([email protected]).