“Federated Meta-Learning” (FML), a concept that allows everyone to benefit from the data that is generated through software libraries including machine learning and data science libraries.
We have built FMLearn, an application developed using the client-server model, to allows the exchange of meta-data about machine learning models for the purpose of meta-learned algorithm selection and configuration.
scikit-learn has been forked and a package has been developed in it to make API calls to FMLearn.
The use of FMLearn to identify the algorithm with the best performance, that is, least MSE for a dataset allows the user in scaling down the repetitive effort and time consumed in rewriting and executing code, correcting possible human errors, etc.
Proposal: Federated Meta-Learning
Publication: Federated Meta-Learning: Democratizing Algorithm Selection Across Disciplines and Software Libraries
GitHub Repo for the modified scikit-learn: mukeshmk/scikit-learn