Clarifying local epoch in the `build_federated_averaging_process` docstring.
@@ -175,12 +175,14 @@ def build_federated_averaging_process(
`tff.learning.ModelWeights` containing the state's model weights.
Each time the `next` method is called, the server model is broadcast to each
- client using a broadcast function. For each client, one epoch of local
- training is performed via the `tf.keras.optimizers.Optimizer.apply_gradients`
- method of the client optimizer. Each client computes the difference between
- the client model after training and the initial broadcast model. These model
- deltas are then aggregated at the server using some aggregation function. The
- aggregate model delta is applied at the server by using the
+ client using a broadcast function. For each client, local training on one
+ pass of the pre-processed client dataset (multiple epochs are possible if the
+ dataset is pre-processed with `repeat` operation) is performed via the
+ `tf.keras.optimizers.Optimizer.apply_gradients` method of the client
+ optimizer. Each client computes the difference between the client model after
+ training and the initial broadcast model. These model deltas are then
+ aggregated at the server using some aggregation function. The aggregate model
+ delta is applied at the server by using the
`tf.keras.optimizers.Optimizer.apply_gradients` method of the server
optimizer.
