Update TFF for research.
Remove TODO
Update links for GCP simulation to point to tensorflow.org documentation
+# Using TFF for Federated Learning Research
+
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If you update those section headings, please make sure you also update
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-# Using TFF for Federated Learning Research
-
-**Note: This page is currently being populated**
-
## Overview
TFF is an extensible, powerful framework for conducting federated learning (FL)
@@ -70,17 +68,15 @@ be solved with federated learning. Datasets include:
## High performance simulations
-<!-- TODO(b/143692319): Referent discussion in the in our paper. -->
-
While the wall-clock time of an FL _simulation_ is not a relevant metric for
evaluating algorithms (as simulation hardware isn't representative of real FL
deployment environments), being able to run FL simulations quickly is critical
for research productivity. Hence, TFF has invested heavily in providing
high-performance single and multi-machine runtimes. Documentation is under
development, but for now see the
-[High-performance simulations with TFF](https://github.com/tensorflow/federated/blob/master/docs/tutorials/simulations.ipynb)
+[High-performance simulations with TFF](https://www.tensorflow.org/federated/tutorials/simulations)
tutorial as well as instructions on
-[setting up simulations with TFF on GCP](https://github.com/tensorflow/federated/blob/master/docs/gcp_setup.md).
+[setting up simulations with TFF on GCP](https://www.tensorflow.org/federated/gcp_setup).
The high-performance TFF runtime is enabled by default.
## TFF for different research areas
