提交 ac45fa60 编辑于 作者: Karan Singhal's avatar Karan Singhal 提交者: tensorflow-copybara
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Add Federated Reconstruction for Matrix Factorization tutorial.

PiperOrigin-RevId: 392999200
上级 2c531538
......@@ -22,6 +22,8 @@ upper_tabs:
path: /federated/tutorials/federated_learning_for_text_generation
- title: "Tuning Recommended Aggregations for Learning"
path: /federated/tutorials/tuning_recommended_aggregators
- title: "Federated Reconstruction for Matrix Factorization"
path: /federated/tutorials/federated_reconstruction_for_matrix_factorization
- heading: Custom computations
- title: "Building Your Own Federated Learning Algorithm"
......
......@@ -11,6 +11,7 @@ exports_files([
"federated_learning_for_image_classification.ipynb",
"federated_learning_for_text_generation.ipynb",
"federated_learning_with_differential_privacy.ipynb",
"federated_reconstruction_for_matrix_factorization.ipynb",
"federated_select.ipynb",
"random_noise_generation.ipynb",
"simulations.ipynb",
......
......@@ -13,11 +13,15 @@ documentation can be found in the [TFF guides](../get_started.md).
* [Federated Learning for text generation](federated_learning_for_text_generation.ipynb)
further demonstrates how to use TFF's FL API to refine a serialized
pre-trained model for a language modeling task.
* [Tuning recommended aggregations for learning](tuning_recommended_aggregators.ipynb)
shows how the basic FL computations in `tff.learning` can be combined with
specialized aggregation routines offering robustness, differential privacy,
compression, and more.
* [Federated Reconstruction for Matrix Factorization](federated_reconstruction_for_matrix_factorization.ipynb)
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.
**Writing custom federated computations**
......
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