fluke简化联邦学习算法研发，让研究者专注核心创新。
fluke: Federated Learning Utility frameworK for Experimentation and research
- 基于Python的轻量级框架，专为快速原型设计优化
- 支持开箱即用，扩展新算法只需少量代码
- 适合关注学习机制的研究者，降低实验门槛
自2016年提出以来，联邦学习（Federated Learning, FL）在机器学习领域广受关注。尽管已有多个框架被提出以促进FL算法开发，但研究者常需从零实现算法及基线实验，原因在于现有框架灵活性不足或扩展成本过高。本文提出 luke，一个专为原型设计优化的Python包，旨在简化新FL算法的开发。fluke面向研究者和实践者，聚焦于联邦系统的学习组件，具有开源特性，可直接使用或以极低代价扩展新算法。
原文摘要 · Abstract (English)
Since its inception in 2016, Federated Learning (FL) has been gaining tremendous popularity in the machine learning community. Several frameworks have been proposed to facilitate the development of FL algorithms, but researchers often resort to implementing their algorithms from scratch, including all baselines and experiments. This is because existing frameworks are not flexible enough to support their needs or the learning curve to extend them is too steep. In this paper, we present \fluke, a Python package designed to simplify the development of new FL algorithms. fluke is specifically designed for prototyping purposes and is meant for researchers or practitioners focusing on the learning components of a federated system. fluke is open-source, and it can be either used out of the box or extended with new algorithms with minimal overhead.
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