Science Score: 64.0%
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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✓Academic publication links
Links to: arxiv.org, zenodo.org -
✓Committers with academic emails
4 of 11 committers (36.4%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (17.3%) to scientific vocabulary
Keywords
Repository
Advanced Privacy-Preserving Federated Learning framework
Basic Info
- Host: GitHub
- Owner: APPFL
- License: mit
- Language: Python
- Default Branch: main
- Homepage: https://appfl.ai
- Size: 69.1 MB
Statistics
- Stars: 148
- Watchers: 7
- Forks: 25
- Open Issues: 12
- Releases: 27
Topics
Metadata Files
README.md
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
- Train any user-defined machine learning model on decentralized data with optional differential privacy and client authentication.
- Simulate various synchronous and asynchronous PPFL algorithms on high-performance computing (HPC) architecture with MPI.
- Implement customizations in a plug-and-play manner for all aspects of FL, including aggregation algorithms, server scheduling strategies, and client local trainers.
Documentation: please check out our documentation for tutorials, users guide, and developers guide.
Table of Contents
:hammerandwrench: Installation
We highly recommend creating a new Conda virtual environment and install the required packages for APPFL.
bash
conda create -n appfl python=3.8
conda activate appfl
User installation
For most users such as data scientists, this simple installation must be sufficient for running the package.
bash
pip install pip --upgrade
pip install "appfl[examples,mpi]"
💡 Note: If you do not need to use MPI for simulations, then you can install the package without the mpi option: 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):
bash
pip install "appfl"
Developer installation
Code developers and contributors may want to work on the local repositofy. To set up the development environment,
bash
git clone --single-branch --branch main https://github.com/APPFL/APPFL.git
cd APPFL
pip install -e ".[mpi,dev,examples]"
💡 Note: If you do not need to use MPI for simulations, then you can install the package without the mpi option: pip install -e ".[dev,examples]".
On Ubuntu:
If the install process failed, you can try:
bash
sudo apt install libopenmpi-dev,libopenmpi-bin,libopenmpi-doc
:bricks: Technical Components
APPFL is primarily composed of the following six technical components
- Aggregator: APPFL supports several popular algorithms to aggregate one or several client local models.
- Scheduler: APPFL supports several synchronous and asynchronous scheduling algorithms at the server-side to deal with different arrival times of client local models.
- Trianer: APPFL supports several client local trainers for various training tasks.
- Privacy: APPFL supports several global/local differential privacy schemes.
- Communicator: APPFL supports MPI for single-machine/cluster simulation, and gRPC and Globus Compute with authenticator for secure distributed training.
- Compressor: APPFL supports several lossy compressors for model parameters, including SZ2, SZ3, ZFP, and SZx.
:bulb: Framework Overview
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.
:pagefacingup: Citation
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} } ```
:trophy: Acknowledgements
This material is based upon work supported by the U.S. Department of Energy, Office of Science, under contract number DE-AC02-06CH11357.
Owner
- Name: APPFL
- Login: APPFL
- Kind: organization
- Location: United States of America
- Repositories: 2
- Profile: https://github.com/APPFL
Argonne Privacy Preserving Federated Learning
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.7.0
title: >-
APPFL: Advanced Privacy-Preserving Federated
Learning
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Minseok
family-names: Ryu
email: minseok.ryu@asu.edu
affiliation: Argonne National Laboratory
- given-names: Kibaek
family-names: Kim
email: kimk@anl.gov
affiliation: Argonne National Laboratory
orcid: 'https://orcid.org/0000-0002-5820-6533'
- given-names: Youngdae
family-names: Kim
email: youngdaekim26@gmail.com
affiliation: ExxonMobil Technology and Engineering Company
- given-names: Zilinghan
family-names: Li
email: zilinghan.li@anl.gov
affiliation: Argonne National Laboratory
- given-names: Sang-il
family-names: Yim
email: yim@anl.gov
affiliation: Argonne National Laboratory
- given-names: Trung-Hieu
faimly-names: Hoang
email: hthieu@illinois.edu
affiliation: University of Illinois at Urbana-Champaign
- given-names: Shourya
family-names: Bose
email: shbose@ucsc.edu
affiliation: University of California, Santa Cruz
- given-names: Shilan
family-names: He
email: shilanh2@illinois.edu
affiliation: University of Illinois at Urbana-Champaign
- given-names: Grant
family-names: Wilkins
email: gfw27@cam.ac.uk
affiliation: University of Cambridge
- given-names: Ravi
family-names: Madduri
email: madduri@anl.gov
affiliation: Argonne National Laboratory
Committers
Last synced: almost 3 years ago
All Time
- Total Commits: 518
- Total Committers: 11
- Avg Commits per committer: 47.091
- Development Distribution Score (DDS): 0.398
Top Committers
| Name | Commits | |
|---|---|---|
| mryu | m****1@g****m | 312 |
| Kibaek Kim | k****k@g****m | 161 |
| Minseok Ryu | 5****u@u****m | 16 |
| Youngdae Kim | y****e@a****v | 11 |
| Sambhav Gupta | s****m@S****l | 5 |
| sam | y****m@a****v | 4 |
| HongdaChen | 6****1@q****m | 3 |
| Zachary Ross | z****5@g****m | 2 |
| yim0331 | y****1@g****m | 2 |
| Youngdae Kim | y****e@g****v | 1 |
| Zachary Ross | z****s@g****v | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 100
- Total pull requests: 285
- Average time to close issues: 3 months
- Average time to close pull requests: 16 days
- Total issue authors: 14
- Total pull request authors: 18
- Average comments per issue: 1.33
- Average comments per pull request: 0.44
- Merged pull requests: 238
- Bot issues: 0
- Bot pull requests: 63
Past Year
- Issues: 24
- Pull requests: 183
- Average time to close issues: 6 days
- Average time to close pull requests: 4 days
- Issue authors: 5
- Pull request authors: 9
- Average comments per issue: 0.33
- Average comments per pull request: 0.19
- Merged pull requests: 155
- Bot issues: 0
- Bot pull requests: 63
Top Authors
Issue Authors
- kibaekkim (39)
- Zilinghan (25)
- minseok-ryu (17)
- yim0331 (4)
- chariako (3)
- shourya01 (3)
- ZeYang1025 (2)
- syedalihasany (1)
- hthieu166 (1)
- zhaozhao626 (1)
- Rene36 (1)
- ben072292 (1)
- InkedWings (1)
- Xinran-Zhao (1)
Pull Request Authors
- Zilinghan (111)
- pre-commit-ci[bot] (51)
- minseok-ryu (26)
- kibaekkim (19)
- kaveenh (16)
- yim0331 (14)
- AdioosinUIUC (12)
- dependabot[bot] (12)
- shourya01 (5)
- rik404 (4)
- samg2003 (4)
- grantwilkins (3)
- carlynlee (2)
- chariako (2)
- youngdae (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 304 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 30
- Total maintainers: 2
pypi.org: appfl
An open-source package for privacy-preserving federated learning
- Homepage: https://github.com/APPFL/APPFL
- Documentation: https://appfl.readthedocs.io/
- License: MIT License
-
Latest release: 1.6.1
published 8 months ago
Rankings
Dependencies
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- nbsphinx *
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- sphinx_rtd_theme *
- grpcio *
- grpcio-tools *
- numpy *
- omegaconf *
- torch *
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- python latest build