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1.7 KiB
Vertical Federated Learning
Vertical Federated Learning (VFL) is a federated machine learning case where multiple data sets share the same sample ID space but differ in feature space.
VFL is supported in BigDL PPML. It allows users to train a federated machine learning model where data features are held by different parties. In BigDL PPML, the following VFL scenarios are supported.
- Private Set Intersection: To get data intersection of different VFL parties.
- Neural Network Model: To train common neural network model with Pytorch or Tensorflow backend across VFL parties.
- FGBoost Model: To train gradient boosted decision tree (GBDT) model across multiple VFL parties.
Quick Start Examples
For each scenario, an quick start example is available in following links.
- Private Set Intersection: A PSI example of getting intersection of two parties
- Pytorch Neural Network Model: An Pytorch based Logistic Regression application by two parties
- FGBoost Model: An federated Gradient Boosted Regression Tree application by two parties
System Architecture
The high-level architecture is shown in the diagram below. This includes the components of the BigDL PPML FL and SGX for Privacy Preservation.
Next steps
For detailed usage of BigDL PPML VFL, please see User Guide