zhxchd/vFedCCE
A vertical federated learning algorithm for classfication problems with gradient-based optimization.
This tool helps data scientists and machine learning engineers collaborate on building classification models using datasets that are split across different organizations. It takes partially overlapping datasets from multiple parties, aligns them, and trains a shared classification model without exposing sensitive raw data from any single party. The output is a robust classification model that benefits from combined features, enabling better predictions for tasks like fraud detection or credit scoring.
No commits in the last 6 months.
Use this if you need to train a classification model using features from multiple data owners, but regulatory or privacy concerns prevent direct data sharing.
Not ideal if your datasets can be combined directly, or if you are training a regression model instead of a classification model.
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8
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2
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Aug 24, 2021
Commits (30d)
0
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