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TensorFlow Federated Tutorials
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track-name="breadcrumb" track-metadata-position="4" track-metadata-eventdetail=""> Tutorials </a></li> </ul><devsite-thumb-rating position="header"> </devsite-thumb-rating> </div> <h1 class="devsite-page-title" tabindex="-1">TensorFlow Federated Tutorials</h1><devsite-feature-tooltip ack-key="AckCollectionsBookmarkTooltipDismiss" analytics-category="Site-Wide Custom Events" analytics-action-show="Callout Profile displayed" analytics-action-close="Callout Profile dismissed" analytics-label="Create Collection Callout" class="devsite-page-bookmark-tooltip nocontent" dismiss-button="true" id="devsite-collections-dropdown" dismiss-button-text="Dismiss" close-button-text="Got it"> <devsite-bookmark></devsite-bookmark> <span slot="popout-heading"> Stay organized with collections </span> <span slot="popout-contents"> Save and categorize content based on your preferences. </span> </devsite-feature-tooltip> <div class="devsite-page-title-meta"> <devsite-view-release-notes></devsite-view-release-notes> </div><devsite-toc class="devsite-nav" depth="2" devsite-toc-embedded> </devsite-toc> <div class="devsite-article-body clearfix "> <p></p><!-- DO NOT EDIT! Automatically generated file. --> <p>These <a href="https://translate.google.com/website?sl=pl&tl=cs&hl=en-GB&u=https://colab.research.google.com/">colab-based</a> tutorials walk you through the main TFF concepts and APIs using practical examples. Reference documentation can be found in the <a href="https://www-tensorflow-org.translate.goog/federated/get_started?_x_tr_sl=pl&_x_tr_tl=cs&_x_tr_hl=en-GB">TFF guides</a>.</p> <aside class="note"> <strong>Note:</strong><span> TFF currently requires Python 3.9 or later, but <a href="https://translate.google.com/website?sl=pl&tl=cs&hl=en-GB&u=https://research.google.com/colaboratory/">Google Colaboratory</a>'s hosted runtimes currently use Python 3.7, and so in order to run these notebooks you will need to use a <a href="https://translate.google.com/website?sl=pl&tl=cs&hl=en-GB&u=https://research.google.com/colaboratory/local-runtimes.html">custom local runtime</a>.</span> </aside> <p><strong>Getting started with federated learning</strong></p> <ul> <li><a href="https://www-tensorflow-org.translate.goog/federated/tutorials/federated_learning_for_image_classification?_x_tr_sl=pl&_x_tr_tl=cs&_x_tr_hl=en-GB">Federated Learning for image classification</a> introduces the key parts of the Federated Learning (FL) API, and demonstrates how to use TFF to simulate federated learning on federated MNIST-like data.</li> <li><a href="https://www-tensorflow-org.translate.goog/federated/tutorials/federated_learning_for_text_generation?_x_tr_sl=pl&_x_tr_tl=cs&_x_tr_hl=en-GB">Federated Learning for text generation</a> further demonstrates how to use TFF's FL API to refine a serialized pre-trained model for a language modeling task.</li> <li><a href="https://www-tensorflow-org.translate.goog/federated/tutorials/tuning_recommended_aggregators?_x_tr_sl=pl&_x_tr_tl=cs&_x_tr_hl=en-GB">Tuning recommended aggregations for learning</a> shows how the basic FL computations in <a href="https://www-tensorflow-org.translate.goog/federated/api_docs/python/tff/learning?_x_tr_sl=pl&_x_tr_tl=cs&_x_tr_hl=en-GB"><code translate="no" dir="ltr">tff.learning</code></a> can be combined with specialized aggregation routines offering robustness, differential privacy, compression, and more.</li> <li><a href="https://www-tensorflow-org.translate.goog/federated/tutorials/federated_reconstruction_for_matrix_factorization?_x_tr_sl=pl&_x_tr_tl=cs&_x_tr_hl=en-GB">Federated Reconstruction for Matrix Factorization</a> introduces partially local federated learning, where some client parameters are never aggregated on the server. The tutorial demonstrates how to use the Federated Learning API to train a partially local matrix factorization model.</li> </ul> <p><strong>Getting started with federated analytics</strong></p> <ul> <li><a href="https://www-tensorflow-org.translate.goog/federated/tutorials/private_heavy_hitters?_x_tr_sl=pl&_x_tr_tl=cs&_x_tr_hl=en-GB">Private Heavy Hitters</a> shows how to use <a href="https://www-tensorflow-org.translate.goog/federated/api_docs/python/tff/analytics/heavy_hitters?_x_tr_sl=pl&_x_tr_tl=cs&_x_tr_hl=en-GB"><code translate="no" dir="ltr">tff.analytics.heavy_hitters</code></a> to build a federated analytics computation to discover private heavy hitters.</li> </ul> <p><strong>Writing custom federated computations</strong></p> <ul> <li><a href="https://www-tensorflow-org.translate.goog/federated/tutorials/building_your_own_federated_learning_algorithm?_x_tr_sl=pl&_x_tr_tl=cs&_x_tr_hl=en-GB">Building Your Own Federated Learning Algorithm</a> shows how to use the TFF Core APIs to implement federated learning algorithms, using Federated Averaging as an example.</li> <li><a href="https://www-tensorflow-org.translate.goog/federated/tutorials/composing_learning_algorithms?_x_tr_sl=pl&_x_tr_tl=cs&_x_tr_hl=en-GB">Composing Learning Algorithms</a> shows how to use the TFF Learning API to easily implement new federated learning algorithms, especially variants of Federated Averaging.</li> <li><a href="https://www-tensorflow-org.translate.goog/federated/tutorials/custom_federated_algorithms_1?_x_tr_sl=pl&_x_tr_tl=cs&_x_tr_hl=en-GB">Custom Federated Algorithms, Part 1: Introduction to the Federated Core</a> and <a href="https://www-tensorflow-org.translate.goog/federated/tutorials/custom_federated_algorithms_2?_x_tr_sl=pl&_x_tr_tl=cs&_x_tr_hl=en-GB">Part 2: Implementing Federated Averaging</a> introduce the key concepts and interfaces offered by the Federated Core API (FC API).</li> <li><a href="https://www-tensorflow-org.translate.goog/federated/tutorials/custom_aggregators?_x_tr_sl=pl&_x_tr_tl=cs&_x_tr_hl=en-GB">Implementing Custom Aggregations</a> explains the design principles behind the <a href="https://www-tensorflow-org.translate.goog/federated/api_docs/python/tff/aggregators?_x_tr_sl=pl&_x_tr_tl=cs&_x_tr_hl=en-GB"><code translate="no" dir="ltr">tff.aggregators</code></a> module and best practices for implementing custom aggregation of values from clients to server.</li> </ul> <p><strong>Simulation best practices</strong></p> <ul> <li><p><a href="https://www-tensorflow-org.translate.goog/federated/tutorials/simulations_with_accelerators?_x_tr_sl=pl&_x_tr_tl=cs&_x_tr_hl=en-GB">TFF simulation with accelerators (GPU)</a> shows how TFF's high-performance runtime can be used with GPUs.</p></li> <li><p><a href="https://www-tensorflow-org.translate.goog/federated/tutorials/working_with_client_data?_x_tr_sl=pl&_x_tr_tl=cs&_x_tr_hl=en-GB">Working with ClientData</a> gives best practices for integrating TFF's <a href="https://www-tensorflow-org.translate.goog/federated/api_docs/python/tff/simulation/datasets/ClientData?_x_tr_sl=pl&_x_tr_tl=cs&_x_tr_hl=en-GB">ClientData</a>-based simulation datasets into TFF computations.</p></li> </ul> <p><strong>Intermediate and advanced tutorials</strong></p> <ul> <li><p><a href="https://www-tensorflow-org.translate.goog/federated/tutorials/random_noise_generation?_x_tr_sl=pl&_x_tr_tl=cs&_x_tr_hl=en-GB">Random noise generation</a> points out some subtleties with using randomness in decentralized computations, and proposes best practices and recommend patterns.</p></li> <li><p><a href="https://www-tensorflow-org.translate.goog/federated/tutorials/federated_select?_x_tr_sl=pl&_x_tr_tl=cs&_x_tr_hl=en-GB">Sending Different Data To Particular Clients With tff.federated_select</a> introduces the <a href="https://www-tensorflow-org.translate.goog/federated/api_docs/python/tff/federated_select?_x_tr_sl=pl&_x_tr_tl=cs&_x_tr_hl=en-GB"><code translate="no" dir="ltr">tff.federated_select</code></a> operator and gives a simple example of a custom federated algorithm that sends different data to different clients.</p></li> <li><p><a href="https://www-tensorflow-org.translate.goog/federated/tutorials/sparse_federated_learning?_x_tr_sl=pl&_x_tr_tl=cs&_x_tr_hl=en-GB">Client-efficient large-model federated learning via federated_select and sparse aggregation</a> shows how TFF can be used to train a very large model where each client device only downloads and updates a small part of the model, using <a href="https://www-tensorflow-org.translate.goog/federated/api_docs/python/tff/federated_select?_x_tr_sl=pl&_x_tr_tl=cs&_x_tr_hl=en-GB"><code translate="no" dir="ltr">tff.federated_select</code></a> and sparse aggregation.</p></li> <li><p><a href="https://www-tensorflow-org.translate.goog/federated/tutorials/federated_learning_with_differential_privacy?_x_tr_sl=pl&_x_tr_tl=cs&_x_tr_hl=en-GB">Federated Learning with Differential Privacy in TFF</a> demonstrates how to use TFF to train models with user-level differential privacy.</p></li> </ul> </div><devsite-thumb-rating position="footer"> </devsite-thumb-rating> <div class="devsite-floating-action-buttons"> </div> </article> <devsite-content-footer class="nocontent"> <p>Except as otherwise noted, the content of this page is licensed under the <a href="https://translate.google.com/website?sl=pl&tl=cs&hl=en-GB&u=https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 License</a>, and code samples are licensed under the <a href="https://translate.google.com/website?sl=pl&tl=cs&hl=en-GB&u=https://www.apache.org/licenses/LICENSE-2.0">Apache 2.0 License</a>. 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