Flower

Overview

Flower is an open-source Python framework for federated learning. It enables training machine learning models across decentralized data sources without centralizing raw data. The framework is designed to be friendly and accessible for developers.

Best for

Best for
Researchers and developers building privacy-preserving distributed machine learning systems

Use cases

• Training models on sensitive client data in healthcare or finance
• Collaborating across organizations without sharing private data
• Simulating and prototyping federated learning experiments

Notes

Flower is an open-source Python framework for federated learning. It enables training machine learning models across decentralized data sources without centralizing raw data. The framework is designed to be friendly and accessible for developers.

6,922 stars on GitHub. Last updated 2026-06-01. Licensed Apache-2.0.

Use cases

• Training models on sensitive client data in healthcare or finance
• Collaborating across organizations without sharing private data
• Simulating and prototyping federated learning experiments

Pros

• Large open-source community with over 6900 GitHub stars
• Python-native framework that integrates easily with existing ML tooling
• Simple, developer-friendly API for federated learning

Cons

• Focused solely on federated learning, not a general observability tool
• Requires setting up and managing a federated infrastructure
• Less mature than some centralized ML frameworks

Indexed from awesome-llmops and enriched against its public facts.