SAP-samples/machine-learning-diff-private-federated-learning
Simulate a federated setting and run differentially private federated learning.
This project helps machine learning engineers and data scientists build and train machine learning models using data from multiple sources without centralizing sensitive information. It takes decentralized datasets and produces a shared model that has learned from all clients, while protecting the privacy of each client's individual data. This is useful for organizations that need to collaborate on model training while adhering to strict privacy regulations.
388 stars. No commits in the last 6 months.
Use this if you need to train a machine learning model collaboratively across different organizations or data silos, but absolutely must protect the privacy of each contributing client's full dataset.
Not ideal if you are looking for a fully-fledged, production-ready federated learning framework with advanced privacy agents for a very large number of clients without manual configuration.
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388
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97
Language
Python
License
Apache-2.0
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Last pushed
Mar 07, 2025
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