def run_one_round(server_state, federated_dataset):
"""Orchestration logic for one round of computation.
Args:
server_state: A `ServerState`.
federated_dataset: A federated `tf.Dataset` with placement `tff.CLIENTS`.
Returns:
A tuple of updated `ServerState` and the result of
`tff.learning.Model.federated_output_computation`.
"""
client_model = tff.federated_broadcast(server_state.model)
client_outputs = tff.federated_map(client_update_fn,
(federated_dataset, client_model))
weight_denom = client_outputs.weights_delta_weight
round_model_delta = tff.federated_mean(client_outputs.weights_delta,
weight=weight_denom)
server_state = tff.federated_apply(server_update_fn,
(server_state, round_model_delta))
aggregated_outputs = dummy_model_for_metadata.federated_output_computation(
client_outputs.model_output)
aggregated_outputs = tff.federated_zip(aggregated_outputs)
return server_state, aggregated_outputs
def run_one_round(server_state, client_states):
"""Orchestration logic for one round of federated training computation."""
# performing the federated averaging of the clients' weights
mean_client_weights = tff.federated_mean(client_states)
print(str(mean_client_weights))
## SERVER UPDATING STEP
server_state = tff.federated_apply(server_update_fn,
(server_state, mean_client_weights))
# returning the new server state
return server_state
def encoded_broadcast_fn(state, value):
"""Broadcast function, to be wrapped as federated_computation."""
state_type = state.type_signature.member
value_type = value.type_signature.member
encode_fn, decode_fn = _build_encode_decode_tf_computations_for_broadcast(
state_type, value_type, encoders)
new_state, encoded_value = tff.federated_apply(encode_fn,
(state, value))
client_encoded_value = tff.federated_broadcast(encoded_value)
client_value = tff.federated_map(decode_fn, client_encoded_value)
return new_state, client_value