Stalactite: Toolbox for Fast Prototyping of Vertical Federated Learning Systems

TL;DR

Stalactite is an open-source framework designed for rapid prototyping of vertical federated learning systems, facilitating research and deployment with built-in algorithms and encryption, demonstrated on real-world datasets.

Contribution

It introduces a comprehensive, easy-to-use VFL toolbox that simplifies prototyping and deployment, with support for multiple algorithms and encryption.

Findings

Supports various VFL algorithms

Includes homomorphic encryption layer

Successfully applied to real-world recommendation data

Abstract

Machine learning (ML) models trained on datasets owned by different organizations and physically located in remote databases offer benefits in many real-world use cases. State regulations or business requirements often prevent data transfer to a central location, making it difficult to utilize standard machine learning algorithms. Federated Learning (FL) is a technique that enables models to learn from distributed datasets without revealing the original data. Vertical Federated learning (VFL) is a type of FL where data samples are divided by features across several data owners. For instance, in a recommendation task, a user can interact with various sets of items, and the logs of these interactions are stored by different organizations. In this demo paper, we present \emph{Stalactite} - an open-source framework for VFL that provides the necessary functionality for building prototypes of VFL systems. It has several advantages over the existing frameworks. In particular, it allows researchers to focus on the algorithmic side rather than engineering and to easily deploy learning in a distributed environment. It implements several VFL algorithms and has a built-in homomorphic encryption layer. We demonstrate its use on a real-world recommendation datasets.