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Add an announcement on the public README about the new fast implementation of DP-SGD.

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Andres Munoz MEdina 2023-02-28 07:45:04 -08:00 committed by A. Unique TensorFlower
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README.md
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@ -11,13 +11,14 @@ issues currently open.
## Latest Updates
2020-12-21: A new
[vectorized version of the TF 2 optimizer](https://github.com/tensorflow/privacy/blob/master/tensorflow_privacy/privacy/optimizers/dp_optimizer_keras_vectorized.py)
is available, which can deliver much faster performance. We recommend trying it
first, and to fall back to using the original non-vectorized version only if
this fails. We are thankful to the
[authors of this paper](https://arxiv.org/abs/2010.09063) for spurring this
change.
2023-02-21: A new implementation of efficient per-example gradient clipping is
now available for
[DP keras models](https://github.com/tensorflow/privacy/tree/master/tensorflow_privacy/privacy/keras_models)
consisting only of Dense and Embedding layers. The models use the fast gradient
calculation results of [this paper](https://arxiv.org/abs/1510.01799). The
implementation should allow for doing DP training without any meaningful memory
or runtime overhead. It also removes the need for tuning the number of
microbatches as it clips the gradient with respect to each example.
## Setting up TensorFlow Privacy
@ -32,11 +33,11 @@ installation documentation).
In addition to TensorFlow and its dependencies, other prerequisites are:
* `scipy` >= 0.17
* `scipy` >= 0.17
* `mpmath` (for testing)
* `mpmath` (for testing)
* `tensorflow_datasets` (for the RNN tutorial `lm_dpsgd_tutorial.py` only)
* `tensorflow_datasets` (for the RNN tutorial `lm_dpsgd_tutorial.py` only)
### Installing TensorFlow Privacy
@ -84,18 +85,16 @@ GitHub pull requests. To speed the code review process, we ask that:
## Tutorials directory
To help you get started with the functionalities provided by this library, we
provide a detailed walkthrough [here](tutorials/walkthrough/README.md) that
will teach you how to wrap existing optimizers
(e.g., SGD, Adam, ...) into their differentially private counterparts using
TensorFlow (TF) Privacy. You will also learn how to tune the parameters
introduced by differentially private optimization and how to
measure the privacy guarantees provided using analysis tools included in TF
Privacy.
provide a detailed walkthrough [here](tutorials/walkthrough/README.md) that will
teach you how to wrap existing optimizers (e.g., SGD, Adam, ...) into their
differentially private counterparts using TensorFlow (TF) Privacy. You will also
learn how to tune the parameters introduced by differentially private
optimization and how to measure the privacy guarantees provided using analysis
tools included in TF Privacy.
In addition, the
`tutorials/` folder comes with scripts demonstrating how to use the library
features. The list of tutorials is described in the README included in the
tutorials directory.
In addition, the `tutorials/` folder comes with scripts demonstrating how to use
the library features. The list of tutorials is described in the README included
in the tutorials directory.
NOTE: the tutorials are maintained carefully. However, they are not considered
part of the API and they can change at any time without warning. You should not
@ -110,8 +109,8 @@ directory, but rather intended as a convenient archive.
## TensorFlow 2.x
TensorFlow Privacy now works with TensorFlow 2! You can use the new
Keras-based estimators found in
TensorFlow Privacy now works with TensorFlow 2! You can use the new Keras-based
estimators found in
`privacy/tensorflow_privacy/privacy/optimizers/dp_optimizer_keras.py`.
For this to work with `tf.keras.Model` and `tf.estimator.Estimator`, however,
@ -120,16 +119,17 @@ you need to install TensorFlow 2.4 or later.
## Remarks
The content of this repository supersedes the following existing folder in the
tensorflow/models [repository](https://github.com/tensorflow/models/tree/master/research/differential_privacy)
tensorflow/models
[repository](https://github.com/tensorflow/models/tree/master/research/differential_privacy)
## Contacts
If you have any questions that cannot be addressed by raising an issue, feel
free to contact:
* Galen Andrew (@galenmandrew)
* Steve Chien (@schien1729)
* Nicolas Papernot (@npapernot)
* Galen Andrew (@galenmandrew)
* Steve Chien (@schien1729)
* Nicolas Papernot (@npapernot)
## Copyright