Are you sure you want to delete this task? Once this task is deleted, it cannot be recovered.
zengdun 283614b722 | 2 years ago | |
---|---|---|
docs | 2 years ago | |
examples | 2 years ago | |
fedlab | 2 years ago | |
LICENSE | 2 years ago | |
README.md | 2 years ago | |
requirements.txt | 2 years ago | |
test_bench.py | 2 years ago |
由谷歌最先提出的联邦学习近来成为机器学习研究中一个迅速发展的领域。联邦学习的目标是在分布式机器学习中保护个体数据隐私,尤其是金融领域、智能医疗以及边缘计算领域。不同于传统的数据中心式的分布式机器学习,联邦学习中的参与者利用本地数据训练本地模型,然后利用具体的聚合策略结合从其他参与者学习到的知识,来合作生成最终的模型。这种学习方式避免了直接分享数据的行为。
为了减轻研究者实现联邦学习算法的负担,我们向大家介绍非常灵活的联邦学习框架FedLab。FedLab为联邦学习的模拟实验提供了必要的模块,包括通信、压缩、模型优化、数据切分,及其他功能性模块。用户们可以像使用乐高积木一样,根据需求构建他们的联邦模拟环境。我们还提供了一些联邦学习的基准算法的实现,方便用户能更好的理解并使用FedLab。
更多FedLab版本的FL算法即将推出。有关更多信息,请关注我们的FedLab基准算法库。
欢迎提交pull request贡献代码。
unittest.TestCase
编写的测试样例如果FedLab对您的研究工作有所帮助,请引用我们的论文:
@article{smile2021fedlab,
title={FedLab: A Flexible Federated Learning Framework},
author={Dun Zeng, Siqi Liang, Xiangjing Hu and Zenglin Xu},
journal={arXiv preprint arXiv:2107.11621},
year={2021}
}
请通过GitHub issues或邮件联系FedLab开发团队:
Dear OpenI User
Thank you for your continuous support to the Openl Qizhi Community AI Collaboration Platform. In order to protect your usage rights and ensure network security, we updated the Openl Qizhi Community AI Collaboration Platform Usage Agreement in January 2024. The updated agreement specifies that users are prohibited from using intranet penetration tools. After you click "Agree and continue", you can continue to use our services. Thank you for your cooperation and understanding.
For more agreement content, please refer to the《Openl Qizhi Community AI Collaboration Platform Usage Agreement》