Generic and Open Learning Federator

April 21, 2025 · View on GitHub

=================================== Generic and Open Learning Federator

.. image:: https://img.shields.io/pypi/v/golf_federated.svg :target: https://pypi.python.org/pypi/golf_federated :alt: PyPI Version

.. image:: https://readthedocs.org/projects/generic-and-open-learning-federator/badge/?version=latest :target: https://generic-and-open-learning-federator.readthedocs.io/en/latest/?version=latest :alt: Documentation Status

.. image:: https://app.travis-ci.com/IntelligentSystemsLab/generic_and_open_learning_federator.svg?token=uyV9JpsFqExQVbjDeQ5q&branch=main :alt: Build Status

A scalable, portable, and lightweight Federated Learning framework.

Free software: MIT license
Documentation: https://generic-and-open-learning-federator.readthedocs.io.

Citations

If this project is helpful to your research, please cite our papers:

L. You, Z. Guo, C. Yuen*, C.Y.C. Chen, Y. Zhang, H.V. Poor,"A framework reforming personalized Internet of Things by federated meta-learning", Nature Communications, 2025. <https://www.nature.com/articles/s41467-025-59217-z>_

L. You, Z. Guo, B. Zuo, Y. Chang*, C. Yuen,"SLMFed: A Stage-based and Layer-wise Mechanism for Incremental Federated Learning to Assist Dynamic and Ubiquitous IoT", IEEE Internet of Things Journal, 2024. <https://ieeexplore.ieee.org/document/10399971>_

S. Liu, L. You*, R. Zhu, B. Liu, R. Liu, Y. Han, C. Yuen,"AFM3D: An Asynchronous Federated Meta-learning Framework for Driver Distraction Detection", IEEE Transactions on Intelligent Transportation Systems, 2024. <https://ieeexplore.ieee.org/document/10423999>_

L. You, S. Liu, B. Zuo, C. Yuen*, D. Niyato, H. V. Poor,"Federated and Asynchronized Learning for Autonomous and Intelligent Things", IEEE Network Magazine, 2023. <https://ieeexplore.ieee.org/document/10274563>_

L. You, S. Liu, T. Wang, B. Zuo, Y. Chang, C. Yuen*,"AiFed: An Adaptive and Integrated Mechanism for Asynchronous Federated Data Mining", IEEE Transactions on Knowledge and Data Engineering, 2023. <https://ieeexplore.ieee.org/document/10316646>_

L. You, S. Liu, Y. Chang, C. Yuen*,"A triple-step asynchronous federated learning mechanism for client activation, interaction optimization, and aggregation enhancement", IEEE Internet of Things Journal, 2022. <https://ieeexplore.ieee.org/document/9815310>_

.. code-block:: sh

@article{You2025framework,
  title={A framework reforming personalized Internet of Things by federated meta-learning},
  author={You, Linlin and Guo, Zihan and Yuen, Chau and Chen, Calvin Yu-Chian and Zhang, Yan and Poor, H. Vincent},
  journal={Nature communications},
  volume={16},
  pages={3739},
  year={2025},
  publisher={Nature Publishing Group UK London}
}

@article{you2024slmfed,
  title={SLMFed: A Stage-Based and Layerwise Mechanism for Incremental Federated Learning to Assist Dynamic and Ubiquitous IoT},
  author={You, Linlin and Guo, Zihan and Zuo, Bingran and Chang, Yi and Yuen, Chau},
  journal={IEEE Internet of Things Journal},
  volume={11},
  number={9},
  pages={16364--16381},
  year={2024},
  publisher={IEEE}
}  

@article{liu2024afm3d,
  title={AFM3D: An asynchronous federated meta-learning framework for driver distraction detection},
  author={Liu, Sheng and You, Linlin and Zhu, Rui and Liu, Bing and Liu, Rui and Yu, Han and Yuen, Chau},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  volume={25},
  number={8},
  pages={9659--9674},
  year={2024},
  publisher={IEEE}
}

@article{you2023federated,
  title={Federated and asynchronized learning for autonomous and intelligent things},
  author={You, Linlin and Liu, Sheng and Zuo, Bingran and Yuen, Chau and Niyato, Dusit and Poor, H Vincent},
  journal={IEEE Network},
  volume={38},
  number={2},
  pages={286--293},
  year={2023},
  publisher={IEEE}
}

@article{you2023aifed,
  title={AiFed: An adaptive and integrated mechanism for asynchronous federated data mining},
  author={You, Linlin and Liu, Sheng and Wang, Tao and Zuo, Bingran and Chang, Yi and Yuen, Chau},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  volume={36},
  number={9},
  pages={4411--4427},
  year={2023},
  publisher={IEEE}
}

@article{you2022triple,
  title={A triple-step asynchronous federated learning mechanism for client activation, interaction optimization, and aggregation enhancement},
  author={You, Linlin and Liu, Sheng and Chang, Yi and Yuen, Chau},
  journal={IEEE Internet of Things Journal},
  volume={9},
  number={23},
  pages={24199--24211},
  year={2022},
  publisher={IEEE}
}

Features

GOLF provides a lightweight solution to support the implementation of FL.
GOLF modularizes system functions to achieve loose coupling during system development and deployment, which makes the framework more generic and scalable.
GOLF uses container technology to ensure that the system is weakly dependent on the compilation environment to achieve portability.
GOLF is compatible with multiple devices (e.g., Android, embedded computers, edge devices, etc.).

News

#. 🌟 June 07, 2024 - Introducing Cedar:

Cedar is a secure, cost-efficient, and domain-adaptive framework for federated meta-learning. Key features include:

💡 Federated Meta-Learning: Enable a safeguarded knowledge transfer with high model generalizability and adaptability.
📨 Cost-Efficient: Implement a layer-wise model uploading mechanism to reduce communication cost.
🔒 Robust Security: Defend against malicious attacks like data inversion and model poisoning.
🔧 High Performance: Support high-performance personalization and customization of globally shareable meta-models.

Installation

To install GOLF, simply use pip:

.. code-block:: sh

pip install golf_federated

Credits

This package was created with Cookiecutter_ and the audreyr/cookiecutter-pypackage_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter .. _audreyr/cookiecutter-pypackage: https://github.com/audreyr/cookiecutter-pypackage

Contributing

We welcome contributions! Here are some ways you can help:

Report bugs and request features on GitHub Issues: https://github.com/IntelligentSystemsLab/generic_and_open_learning_federator/issues
Submit pull requests to improve the codebase.

Contact

For any questions or issues, please contact the development team at guozh29@mail2.sysu.edu.cn.