https://github.com/huawei-noah/federated-learning
https://github.com/huawei-noah/federated-learning
Last synced: 6 months ago
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- Host: GitHub
- URL: https://github.com/huawei-noah/federated-learning
- Owner: huawei-noah
- Created: 2023-04-23T01:42:25.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-05-20T03:43:16.000Z (over 1 year ago)
- Last Synced: 2025-03-23T23:26:45.268Z (7 months ago)
- Language: Python
- Size: 306 KB
- Stars: 19
- Watchers: 2
- Forks: 4
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
The repository is for federated learning and transfer learning projects inside Huawei Noah's Ark Lab. Currently it includes:
* FairFL: a fair federated learning benchmarking framework that implements existing fair FL algorithms in the paper "[Proportional Fairness in Federated Learning](https://openreview.net/forum?id=ryUHgEdWCQ)" by Guojun Zhang, Saber Malekmohammadi, Xi Chen and Yaoliang Yu. Comments are welcome!
* Hessian Alignment: domain generalization algorithms based on Hessian alignment (ICCV 2023). The related paper is "[Understanding Hessian Alignment for Domain Generalization](https://arxiv.org/abs/2308.11778)" by Sobhan Hemati*, Guojun Zhang*, Amir Estiri and Xi Chen. We have just released the code.
* Federated Domain Generalization: Incorporating domain generalization in the federated learning setting, based on the paper "[Mitigating Data Heterogeneity in Federated Learning with Data Augmentation](https://arxiv.org/abs/2206.09979)" by Artur Back de Luca*, Guojun Zhang*, Xi Chen, Yaoliang Yu.
* Layer Normalization: a federated learning benchmark that compares relevant FL algorithms for label shift problems, studied in the paper "[Understanding the Role of Layer Normalization in Label-Skewed Federated Learning](https://openreview.net/forum?id=6BDHUkSPna)" by Guojun Zhang, Mahdi Beitollahi, Alex Bie, Xi Chen.
If you want to include your algorithms in one of our benchmarks, please make a pull request.
This open source project is not an official Huawei product, Huawei is not expected to provide support for this project.