# HFL Model Training Overview

Horizontal Federated Learning (HFL) is used in scenarios where datasets share the same feature space (same type of columns) but differ in samples (different rows).

![HFL vs VFL overview](/_static/hfl-vs-vfl.jpg)

Key features of HFL:

* Data never leaves client nodes.
* Datasets have the same features, but different records. All the clients hold label information.
* Computation happens client-side; server only  does aggregation.
* After training, each client can get a copy of the global model.

![HFL overview](/_static/hfl-overview.jpg)

## HFL example use case

An example of an HFL use case is Insurance Home Inspection.

A insurance carrier has a model predicting the number of issues that will be identified through home inspection in specific regions in Canada. They would like to *expand* their model's scope by using a data provider's nationwide inspection data sets and determine model boost before purchasing.


## Available HFL models

integrate.ai supports the following HFL model types:

* [FFNet](/hfl-train.md) - Feed forward neural network
* [Generalized linear models (GLM)](/hfl-train-glm.md)
* [Gradient Boosted Models (GBM)](/hfl-gbm.md)
* [Making predictions](/hfl-predict-tutorial.md)

Vertical Federated Learning (VFL) is also supported. [Click here](/train-overview-vfl.md) to learn more.