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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.
For each scenario, some quick starts are available in above links.
Key Concepts
A FL Server is a gRPC server to handle requests from FL Client. A FL Client is a gRPC client to send requests to FL Server. These requests include:
- serialized model to use in training at FL Server
- some model related instance, e.g. loss function, optimizer
- the Tensor which FL Server and FL Client interact, e.g. predict output, label, gradient
A FL Context is a singleton holding a FL Client instance. By default, only one instance is held in a FL application. And the gRPC channel in this singleton instance could be reused in multiple algorithms.