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Flower Hub: A Reproducible Benchmarking Platform for Federated Learning in Simulation and Deployment

arXiv · AI, language, vision and robotics · article · Aug 25, 2026 · UTC

Federated learning (FL) has emerged as a key approach for training models across decentralized data, yet benchmarking in FL remains difficult to reproduce, compare, and extend. Existing evaluations are often tied to custom infrastructure, released as incomplete research code, and conducted primarily in simulation, which limits portability and practical relevance. We present Flower Hub, a platform for publishing, discovering, and executing decentralized and federated applications. We show how it enables reproducible benchmarking by packaging benchmarks as executable, versioned applications with

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