交互式网页工具，让零基础用户直观理解联邦学习核心挑战。
Federated Learning Playground
- 浏览器内无代码实验异构数据、超参数与聚合算法
- 实时可视化展示非独立同分布数据对模型的影响
- 适合初学者入门，也供研究者快速对比联邦学习方法
我们提出 Federated Learning Playground，一个受 TensorFlow Playground 启发并扩展的交互式浏览器平台，用于教学核心联邦学习（FL）概念。用户无需编码或系统配置，即可在浏览器中直接实验异构客户端数据分布、模型超参数和聚合算法，并通过实时可视化观察其对客户端和全局模型的影响，从而获得对非独立同分布数据、本地过拟合及可扩展性等挑战的直观理解。该平台作为易用的教育工具，降低了分布式人工智能的入门门槛，同时为快速原型设计和比较联邦学习方法提供了沙盒环境。通过普及联邦学习的探索，推动这一重要范式的更广泛理解和应用。
原文摘要 · Abstract (English)
We present Federated Learning Playground, an interactive browser-based platform inspired by and extends TensorFlow Playground that teaches core Federated Learning (FL) concepts. Users can experiment with heterogeneous client data distributions, model hyperparameters, and aggregation algorithms directly in the browser without coding or system setup, and observe their effects on client and global models through real-time visualizations, gaining intuition for challenges such as non-IID data, local overfitting, and scalability. The playground serves as an easy to use educational tool, lowering the entry barrier for newcomers to distributed AI while also offering a sandbox for rapidly prototyping and comparing FL methods. By democratizing exploration of FL, it promotes broader understanding and adoption of this important paradigm.
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