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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.
+
+
+## Lifecycle
+
+## Fault Tolerance