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[2602.19489] Federated Learning Playground
Summary
The article presents the Federated Learning Playground, an interactive platform designed to teach core concepts of Federated Learning through real-time visualizations and experimentation without coding requirements.
Why It Matters
This platform democratizes access to Federated Learning, making it easier for newcomers to understand and experiment with complex concepts. By lowering the entry barrier, it encourages broader adoption and exploration of distributed AI methodologies, which are crucial for privacy-preserving machine learning.
Key Takeaways
- Federated Learning Playground allows users to experiment with FL concepts interactively.
- The platform requires no coding, making it accessible to beginners.
- Real-time visualizations help users understand challenges like non-IID data and local overfitting.
- It serves as both an educational tool and a sandbox for prototyping FL methods.
- The initiative promotes wider understanding and adoption of Federated Learning.
Computer Science > Machine Learning arXiv:2602.19489 (cs) [Submitted on 23 Feb 2026] Title:Federated Learning Playground Authors:Bryan Guanrong Shan, Alysa Ziying Tan, Han Yu View a PDF of the paper titled Federated Learning Playground, by Bryan Guanrong Shan and 2 other authors View PDF HTML (experimental) 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. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2602.19489 [cs.LG] (or arXiv:2602.19489v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2602.19489 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Bryan Shan [view email] [v1] Mon, 23 Feb 2026 ...
