{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# What is Federated Learning?\n", "\n", "Welcome to the Flower federated learning tutorial!\n", "\n", "In this tutorial, you will learn what federated learning is, build your first system in Flower, and gradually extend it. If you work through all parts of the tutorial, you will be able to build advanced federated learning systems that approach the current state of the art in the field.\n", "\n", "🧑🏫 This tutorial starts at zero and expects no familiarity with federated learning. Only a basic understanding of data science and Python programming is assumed.\n", "\n", "> [Star Flower on GitHub](https://github.com/adap/flower) ⭐️ and join the open-source Flower community on Slack to connect, ask questions, and get help: [Join Slack](https://flower.ai/join-slack) 🌼 We'd love to hear from you in the `#introductions` channel! And if anything is unclear, head over to the `#questions` channel.\n", "\n", "Let's get started!" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Classic machine learning\n", "\n", "Before we begin to discuss federated learning, let us quickly recap how most machine learning works today.\n", "\n", "In machine learning, we have a model, and we have data. The model could be a neural network (as depicted here), or something else, like classical linear regression.\n", "\n", "