alexjungaalto/FederatedLearning
Material workbench for the master-level course CS-E4740 "Federated Learning"
This course material provides an introduction to Federated Learning (FL), a method for training machine learning models on data distributed across many devices or organizations without centralizing the raw data. It teaches how to design privacy-preserving and scalable FL algorithms. Master's level students, machine learning practitioners, and researchers interested in decentralized AI would use this.
197 stars.
Use this if you need to understand and apply machine learning techniques where data privacy and distribution are critical concerns, such as in healthcare or recommendation systems.
Not ideal if you are looking for a simple, centralized machine learning solution or a tool for immediate, low-code model deployment.
Stars
197
Forks
64
Language
Jupyter Notebook
License
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Category
Last pushed
Mar 12, 2026
Commits (30d)
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