sanchi-shirur4/federated-learning-blockchain
The Project's goal is to simulate a decentralised approach to building machine learning models while protecting the privacy of users.
This project helps machine learning engineers and data scientists build predictive models using data from multiple sources (like mobile phones or laptops) without directly sharing sensitive user information. It takes local model training results from individual devices and aggregates them to improve a central model, enhancing privacy. The output is a more accurate, collectively trained machine learning model.
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Use this if you need to train a machine learning model on decentralized user data while prioritizing data privacy and avoiding direct aggregation of raw sensitive information.
Not ideal if your machine learning workflow involves training on readily available, centralized, and non-sensitive data, or if you require real-time, high-throughput model updates from individual devices.
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Language
Python
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Last pushed
Sep 10, 2021
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