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Federated learning is a machine learning method that allows secure and private data sharing, says Ezequiel Lanza. “This means that models can be trained on a user’s device without having to send data to a centralized server.”
Open Federated Learning (OpenFL) is a Python 3 framework for implementing federated learning models, designed to be a flexible and easy-to-learn tool for data scientists.
“By using OpenFL, researchers can easily build models that are secure and private while still being able to leverage the power of distributed computing,” Lanza says.
This article introduces core components of OpenFL and shares specifics of what it can do.
Read more at Opensource.net.
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