General streamlining of `optimization` to avoid directory bloat.
This change removes centralized training binaries, and organizes all federated task configuration files under a single folder.
@@ -109,7 +109,7 @@ in the
[README](https://github.com/tensorflow/federated/blob/master/tensorflow_federated/python/examples/simple_fedavg/README.md).
A more general implementation of Federated Averaging can be found
-[here](https://github.com/google-research/federated/blob/master/optimization/shared/fed_avg_schedule.py).
+[here](https://github.com/google-research/federated/blob/master/optimization/fed_avg_schedule.py).
This implementation allows for more sophisticated optimization techniques,
including learning rate scheduling and the use of different optimizers on both
the server and client. Code that applies this generalized Federated Averaging to
