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docs: First steps with federated learning
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src/federated-learning/docs/jupyter-notebooks/introduction_fl.ipynb
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{ | ||
"cells": [ | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"\n", | ||
"# **FEDERATED LEARNING**\n", | ||
"---" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"En esta sección se explicarán una breve introducción, notas, etc. de Federated Learning. Se comenzará definiendo qué es y como se puede utilizar (casos de uso).\n", | ||
"\n", | ||
"Los primeros enlaces de interés para este documento son los siguientes:\n", | ||
"\n", | ||
"- Federated Learning Wiki, Wikipedia -> [here](https://en.wikipedia.org/wiki/Federated_learning)\n", | ||
"- What is Federated Learning? Blog, IBM -> [here](https://research.ibm.com/blog/what-is-federated-learning)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [] | ||
} | ||
], | ||
"metadata": { | ||
"language_info": { | ||
"name": "python" | ||
} | ||
}, | ||
"nbformat": 4, | ||
"nbformat_minor": 2 | ||
} |
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