microsoft/PersonalizedFL
Personalized federated learning codebase for research
This toolkit helps researchers explore and compare different federated learning algorithms. It takes decentralized datasets, such as medical images or activity data from various individuals, and trains machine learning models without directly accessing private information. The output is a robust, privacy-preserving model, making it ideal for machine learning researchers focusing on privacy-preserving AI.
412 stars. No commits in the last 6 months.
Use this if you are a researcher developing or benchmarking personalized federated learning algorithms on decentralized data, especially in fields like healthcare or activity recognition.
Not ideal if you are looking for a production-ready federated learning system, as this is designed mainly for research and quick experimentation.
Stars
412
Forks
51
Language
Python
License
MIT
Category
Last pushed
Oct 04, 2023
Commits (30d)
0
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