Federated Learning Playground

• URL: http://arxiv.org/abs/2602.19489v1
• Date: Mon, 23 Feb 2026 04:14:40 GMT
• Title: Federated Learning Playground
• Authors: Bryan Guanrong Shan, Alysa Ziying Tan, Han Yu,
• Abstract summary: We present Federated Learning Playground, an interactive browser-based platform that teaches core Federated Learning (FL) concepts.<n>Users can experiment with heterogeneous client data distributions and aggregation algorithms directly in the browser without coding or system setup.<n>The playground serves as an easy to use educational tool, lowering the entry barrier for newcomers to distributed AI.
• Score: 13.518363925108867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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