A lighting-fast and developer-friendly Federated Learning SDK
Hello everyone We have been working for a couple of years now on MetisFL, a federated learning framework that allows developers to federate their machine learning workflows and train their models across distributed datasets without having to collect the data in a centralized location. Since the project is now transitioning to a public phase, we are actively encouraging developers, researchers and data scientists to experiment with the framework and contribute to the codebase. Thank you in advance!
A lighting-fast and developer-friendly Federated Learning SDK launched on July 10, 2023 with 13 votes, #1,554 of 4,347 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 3 years on.
In plain words
MetisFL is a federated learning framework that enables developers to train machine learning models across distributed datasets without centralizing data. The SDK allows machine learning workflows to be federated, letting researchers and data scientists build models while keeping data in its original locations. Designed for developers and researchers, MetisFL is now in public beta and open for experimentation and community contributions.
written from the facts on this page · September 2026
From the sources
The first open Federated Learning framework implemented in C++ and Python. - bioint/MetisFL
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