JonasGeiping/breaching
Breaching privacy in federated learning scenarios for vision and text
This framework helps data scientists and machine learning engineers evaluate privacy risks in federated learning systems. It takes in shared model updates from individual users and outputs the private training data that was used to generate those updates. This allows organizations deploying federated learning to understand how vulnerable their systems are to data reconstruction attacks and identify potential privacy breaches.
313 stars. Available on PyPI.
Use this if you are building or deploying federated learning models and need to assess their susceptibility to data privacy attacks.
Not ideal if you are looking for defenses or full end-to-end federated model training simulations, as this tool focuses purely on evaluating attack effectiveness.
Stars
313
Forks
73
Language
Python
License
MIT
Category
Last pushed
Jan 24, 2026
Commits (30d)
0
Dependencies
5
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