Library / Framework · Python

flwr-trees

Federated learning with tree-based models (Random Forest, XGBoost, GBT) using Flower — scikit-learn compatible API

Mohammad Ahmadullah Khan@MAUK9086 · Atlas 69.2 · #9= builder

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What it is

flwr-trees is a Python library that provides scikit-learn-compatible federated estimators for tree-based models (Random Forest, XGBoost, Gradient Boosted Trees) using the Flower framework. It includes strategies for federated learning, simulation utilities, and privacy-preserving extensions.

differential-privacy
federated-learning
flower-fl
machine-learning
python
random-forest
scikit-learn
xgboost

Scored atfc608119 Nov 2026Changes since

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Where it counts

Machine Learning80%
MLOps50%
Data Science20%

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What the judge found

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Strengths4

Comprehensive library for federated tree-based models with multiple strategies (bagging, cyclic, histogram aggregation).
Well-documented with clear API, architecture explanations, and practical examples.
Includes valuable features like communication-efficient histogram aggregation and differential privacy wrappers.
Modular code structure and passing test suite verified by CI.

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