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README.md

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🎯***We create a beginner-friendly algorithm library and benchmark platform for those new to federated learning. Join us in expanding the FL community by contributing your algorithms, datasets, and metrics to this project.***
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👏 **PFLlib is using Apache License.**
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👏 **PFLlib now has its official website and domain name: https://www.pfllib.com/!!!**
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👏 **The **[Leaderboard](http://www.pfllib.com/benchmark.html)** is live! Our methods—[FedCP](https://github.com/TsingZ0/FedCP), [GPFL](https://github.com/TsingZ0/GPFL), and [FedDBE](https://github.com/TsingZ0/DBE)—lead the way. Notably, **FedDBE** stands out with robust performance across varying data heterogeneity levels.**
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[![arXiv](https://img.shields.io/badge/arXiv-2312.04992-b31b1b.svg)](https://arxiv.org/abs/2312.04992) [![License: GPL v2](https://img.shields.io/badge/License-GPL_v2-blue.svg)](https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html)
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[![arXiv](https://img.shields.io/badge/arXiv-2312.04992-b31b1b.svg)](https://arxiv.org/abs/2312.04992) ![Apache License 2.0](https://img.shields.io/badge/license-Apache%202.0-blue.svg)
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![](./structure.png)
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Figure 1: An Example for FedAvg. You can create a scenario using `generate_DATA.py` and run an algorithm using `main.py`, `clientNAME.py`, and `serverNAME.py`. For a new algorithm, you only need to add new features in `clientNAME.py` and `serverNAME.py`.

docs/docs.html

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<a href="https://arxiv.org/abs/2312.04992">
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<img src="https://img.shields.io/badge/arXiv-2312.04992-b31b1b.svg" alt="arXiv Badge">
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</a>
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<a href="https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html">
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<img src="https://img.shields.io/badge/License-GPL_v2-blue.svg" alt="License Badge">
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<a href="http://www.apache.org/licenses/LICENSE-2.0">
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<img src="https://img.shields.io/badge/license-Apache%202.0-blue.svg" alt="Apache License 2.0" />
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</a>
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<br/>
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