apple/pfl-research
Simulation framework for accelerating research in Private Federated Learning
This framework helps machine learning researchers quickly test new ideas in private federated learning (PFL). Researchers input their existing models and data, and the framework simulates how they perform in a federated learning environment, providing results and benchmarks. It is designed for machine learning researchers and scientists experimenting with new PFL algorithms and privacy-preserving techniques.
352 stars.
Use this if you are a researcher developing and testing novel privacy-preserving federated learning algorithms and need a robust simulation environment.
Not ideal if you are looking to deploy a federated learning system in a production environment for third-party use.
Stars
352
Forks
40
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Mar 02, 2026
Commits (30d)
0
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