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New Python Packaging Proposal Aims to Solve Phantom Dependency Problem with SBOMs
PEP 770 proposes adding SBOM support to Python packages to improve transparency and catch hidden non-Python dependencies that security tools often miss.
APPFL - Advanced Privacy-Preserving Federated Learning Framework.
APPFL, Advanced Privacy-Preserving Federated Learning, is an open-source and highly extensible software framework that allows research communities to implement, test, and validate various ideas related to privacy-preserving federated learning (FL), and deploy real FL experiments easily and safely among distributed clients to train more robust ML models.With this framework, developers and users can easily
Documentation: please check out our documentation for tutorials, users guide, and developers guide.
We highly recommend creating a new Conda virtual environment and install the required packages for APPFL.
conda create -n appfl python=3.8
conda activate appfl
For most users such as data scientists, this simple installation must be sufficient for running the package.
pip install pip --upgrade
pip install "appfl[examples]"
If we want to even minimize the installation of package dependencies, we can skip the installation of a few packages (e.g., matplotlib
and jupyter
):
pip install "appfl"
Code developers and contributors may want to work on the local repositofy. To set up the development environment,
git clone --single-branch --branch main https://github.com/APPFL/APPFL.git
cd APPFL
pip install -e ".[dev,examples]"
On Ubuntu: If the install process failed, you can try:
sudo apt install libopenmpi-dev,libopenmpi-bin,libopenmpi-doc
APPFL is primarily composed of the following six technical components
In the design of the APPFL framework, we essentially create the server agent and client agent, using the six technical components above as building blocks, to act on behalf of the FL server and clients to conduct FL experiments. For more details, please refer to our documentation.
If you find APPFL useful for your research or development, please consider citing the following papers:
@article{li2024advances,
title={Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework},
author={Li, Zilinghan and He, Shilan and Yang, Ze and Ryu, Minseok and Kim, Kibaek and Madduri, Ravi},
journal={arXiv preprint arXiv:2409.11585},
year={2024}
}
@inproceedings{ryu2022appfl,
title={APPFL: open-source software framework for privacy-preserving federated learning},
author={Ryu, Minseok and Kim, Youngdae and Kim, Kibaek and Madduri, Ravi K},
booktitle={2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
pages={1074--1083},
year={2022},
organization={IEEE}
}
This material is based upon work supported by the U.S. Department of Energy, Office of Science, under contract number DE-AC02-06CH11357.
FAQs
An open-source package for privacy-preserving federated learning
We found that appfl demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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