Summary of Stalactite: Toolbox For Fast Prototyping Of Vertical Federated Learning Systems, by Anastasiia Zakharova et al.
Stalactite: Toolbox for Fast Prototyping of Vertical Federated Learning Systems
by Anastasiia Zakharova, Dmitriy Alexandrov, Maria Khodorchenko, Nikolay Butakov, Alexey Vasilev, Maxim Savchenko, Alexander Grigorievskiy
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning framework called Stalactite enables building prototypes of Vertical Federated Learning (VFL) systems without transferring sensitive data to a central location. This technique, which combines features from multiple organizations’ datasets, is particularly useful in scenarios where state regulations or business requirements prevent data sharing. By allowing researchers to focus on algorithmic development rather than infrastructure engineering, Stalactite simplifies the deployment of VFL-based solutions in distributed environments. The framework includes implementations of various VFL algorithms and a built-in homomorphic encryption layer for added security. To demonstrate its capabilities, we apply Stalactite to a real-world recommendation dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Stalactite is a special tool that helps people build new kinds of machine learning systems without sharing sensitive data with others. Imagine you have lots of different pieces of information that are all related to each other, but they’re stored in different places by different organizations. Stalactite makes it possible to combine these pieces of information to learn something new without having to move the data around or share it with anyone. This is especially important when there are laws or rules that prevent sharing sensitive data. The tool is designed to make it easy for researchers to focus on the science behind machine learning, rather than getting bogged down in technical details. |
Keywords
» Artificial intelligence » Federated learning » Machine learning
