XGBoost

A privacy-preserving federated implementation of the XGBoost library for parallel decision tree boosting. The scikit-learn API is supported (🔗Python API Reference — xgboost 1.6.1 documentation).

Operation

• Use the XGBoost train(), predict(), and predict_proba() methods.
• When using add_agreement() to forge an agreement on a trained XGBoost Model, use Operation.EXECUTE for the operation parameter.
• When using add_agreement() to allow a counterparty to use your dataset for model training, or when using create_job() to train an XGBoost Model, use Operation.XGBOOST_TRAIN for the operation parameter.

Parameters

Training parameters

• dataset_path: str = ""
• regression: bool = False
• xgboost_params: Dict[str, Union[int, str]] = None
• • Refer to the 🔗scikit-learn wrapper in the docs.
• test_size: float = 0.0

Inference parameters

• max_depth: int = -1
• features: int = -1
• model_path: str = ""
• regression: bool = False
• predict_proba: bool = False
• dataset_path: str = ""

Limitations

• Only gbtree booster is supported in SMPC inference.
• Higher depth forests will cause extreme memory usage when used via SMPC.