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_round_num = tff.federated_broadcast(server_state.round_num)
client_outputs = tff.federated_map(
client_update_fn,
(federated_dataset, client_model, client_round_num))
client_weight = client_outputs.client_weight
model_delta = tff.federated_mean(
client_outputs.weights_delta, weight=client_weight)
server_state = tff.federated_map(server_update_fn,
(server_state, model_delta))
aggregated_outputs = dummy_model.federated_output_computation(
client_outputs.model_output)
if aggregated_outputs.type_signature.is_struct():
aggregated_outputs = tff.federated_zip(aggregated_outputs)
return server_state, aggregated_outputs
def run_one_round(server_state, federated_dataset):
"""Orchestration logic for one round of TrieHH computation.
Args:
server_state: A `ServerState`.
federated_dataset: A federated `tf.Dataset` with placement `tff.CLIENTS`.
Returns:
An updated `ServerState`
"""
discovered_prefixes = tff.federated_broadcast(
server_state.discovered_prefixes)
round_num = tff.federated_broadcast(server_state.round_num)
client_outputs = tff.federated_map(
client_update_fn,
(federated_dataset, discovered_prefixes, round_num))
accumulated_votes = tff.federated_sum(client_outputs.client_votes)
accumulated_weights = tff.federated_sum(client_outputs.client_weight)
server_state = tff.federated_map(
server_update_fn,
(server_state, accumulated_votes, accumulated_weights))
server_output = tff.federated_value([], tff.SERVER)
return server_state, server_output
def federated_train(model, learning_rate, data, classes):
return tff.federated_mean(
tff.federated_map(local_train, [
tff.federated_broadcast(model),
tff.federated_broadcast(learning_rate), data,
tff.federated_broadcast(classes)
]))
def run_one_round(server_state, client_states, federated_dataset,
federated_dataset_single):
from_server = FromServer(w=server_state.w,
meta_w=server_state.meta_w,
round_num=server_state.round_num)
from_server = tff.federated_broadcast(from_server)
control_output = tff.federated_map(
control_computation,
(federated_dataset_single, client_state, from_server))
c = tff.federated_broadcast(
tff.federated_mean(control_output.w_delta,
weight=control_output.client_weight))
@tff.tf_computation(client_output_type.w_delta,
client_output_type.w_delta)
def compute_control_input(c, c_i):
correction = tf.nest.map_structure(lambda a, b: a - b, c, c_i)
# if we are using SCAFFOLD then use the correction, otherwise
# we just let the correction be zero.
return tf.cond(
tf.constant(control, dtype=tf.bool), lambda: correction,
lambda: tf.nest.map_structure(tf.zeros_like, correction))
# the collection of gradient corrections
corrections = tff.federated_map(compute_control_input,
(c, control_output.cs))
client_outputs = tff.federated_map(
client_computation,
(federated_dataset, from_server, client_state, corrections))
w_delta = tff.federated_mean(client_outputs.w_delta,
weight=client_outputs.client_weight)
server_state = tff.federated_map(server_computation,
(server_state, w_delta))
return (server_state, client_outputs.client_state)
def federated_train(model, learning_rate, data):
l = tff.federated_map(
local_train,
[tff.federated_broadcast(model),
tff.federated_broadcast(learning_rate),
data])
return l
def federated_train(model, lr, data):
#返回的是训练后的模型
return tff.federated_mean(
tff.federated_map(local_train, [
tff.federated_broadcast(model),
tff.federated_broadcast(lr), data
]))
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_round_num = tff.federated_broadcast(server_state.round_num)
client_optimizer_weights = tff.federated_broadcast(
server_state.optimizer_state)
client_outputs = tff.federated_map(
client_update_fn, (federated_dataset, client_model,
client_round_num, client_optimizer_weights))
client_weight = client_outputs.client_weight
model_delta = tff.federated_mean(client_outputs.weights_delta,
weight=client_weight)
implicit_mean_broad = tff.federated_broadcast(
tff.federated_mean(client_outputs.weights, weight=client_weight))
@tff.tf_computation(model_weights_type.trainable,
model_weights_type.trainable)
def individual_drift_fn(client_model, implicit_mean):
drifts = tf.nest.map_structure(lambda a, b: a - b, client_model,
implicit_mean)
return drifts
drifts = tff.federated_map(
individual_drift_fn, (client_outputs.weights, implicit_mean_broad))
@tff.tf_computation(model_weights_type.trainable)
def individual_drift_norm_fn(delta_from_mean):
drift_norm = tf.reduce_sum(
tf.nest.flatten(
tf.nest.map_structure(lambda a: tf.nn.l2_loss(a),
delta_from_mean)))
return drift_norm
drift_norms = tff.federated_map(individual_drift_norm_fn, drifts)
client_drift = tff.federated_mean(drift_norms, weight=client_weight)
server_state = tff.federated_map(
server_update_fn, (server_state, model_delta, client_drift))
aggregated_outputs = dummy_model.federated_output_computation(
client_outputs.model_output)
if aggregated_outputs.type_signature.is_struct():
aggregated_outputs = tff.federated_zip(aggregated_outputs)
return server_state, aggregated_outputs
def run_one_round(server_state, federated_dataset):
"""Orchestration logic for one round of computation.
Note that in addition to updating the server weights according to the client
model weight deltas, we extract metrics (governed by the `monitor` attribute
of the `client_lr_callback` and `server_lr_callback` attributes of the
`server_state`) and use these to update the client learning rate callbacks.
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` before and during local
client training.
"""
client_model = tff.federated_broadcast(server_state.model)
client_lr = tff.federated_broadcast(
server_state.client_lr_callback.learning_rate)
if dataset_preprocess_comp is not None:
federated_dataset = tff.federated_map(dataset_preprocess_comp,
federated_dataset)
client_outputs = tff.federated_map(
client_update_fn, (federated_dataset, client_model, client_lr))
client_weight = client_outputs.client_weight
aggregated_gradients = tff.federated_mean(
client_outputs.accumulated_gradients, weight=client_weight)
initial_aggregated_outputs = dummy_model.federated_output_computation(
client_outputs.initial_model_output)
if isinstance(initial_aggregated_outputs.type_signature,
tff.StructType):
initial_aggregated_outputs = tff.federated_zip(
initial_aggregated_outputs)
aggregated_outputs = dummy_model.federated_output_computation(
client_outputs.model_output)
if isinstance(aggregated_outputs.type_signature, tff.StructType):
aggregated_outputs = tff.federated_zip(aggregated_outputs)
client_monitor_value = initial_aggregated_outputs[client_monitor]
server_monitor_value = initial_aggregated_outputs[server_monitor]
server_state = tff.federated_map(
server_update_fn, (server_state, aggregated_gradients,
client_monitor_value, server_monitor_value))
result = collections.OrderedDict(
before_training=initial_aggregated_outputs,
during_training=aggregated_outputs)
return server_state, result
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`.
"""
# Run computation on the clients.
client_model = tff.federated_broadcast(server_state.model)
client_round_num = tff.federated_broadcast(server_state.round_num)
client_outputs = tff.federated_map(
client_update_fn,
(federated_dataset, client_model, client_round_num))
# Aggregate model deltas.
client_weight = client_outputs.client_weight
if mask_zeros_in_client_updates:
model_delta = federated_mean_masked(client_outputs.weights_delta,
client_weight)
else:
model_delta = tff.federated_mean(client_outputs.weights_delta,
client_weight)
server_state = tff.federated_map(server_update_fn,
(server_state, model_delta))
# Aggregate model outputs that contain local metrics and various statistics.
aggregated_outputs = placeholder_model.federated_output_computation(
client_outputs.model_output)
additional_outputs = tff.federated_mean(
client_outputs.additional_output, weight=client_weight)
@tff.tf_computation(aggregated_outputs.type_signature.member,
additional_outputs.type_signature.member)
def _update_aggregated_outputs(aggregated_outputs, additional_outputs):
aggregated_outputs.update(additional_outputs)
return aggregated_outputs
aggregated_outputs = tff.federated_map(
_update_aggregated_outputs,
(aggregated_outputs, additional_outputs))
if aggregated_outputs.type_signature.is_struct():
aggregated_outputs = tff.federated_zip(aggregated_outputs)
return server_state, aggregated_outputs
def run_one_round(server_state, federated_dataset):
"""Orchestration logic for one round of computation.
Args:
server_state: A `ServerState` containing the state of the training process
up to the current round.
federated_dataset: A federated `tf.data.Dataset` with placement
`tff.CLIENTS` containing data to train the current round on.
Returns:
A tuple of updated `ServerState` and `tf.Tensor` of average loss.
"""
server_message = tff.federated_map(server_message_fn, server_state)
server_message_at_client = tff.federated_broadcast(server_message)
client_outputs = tff.federated_map(
client_update_fn, (federated_dataset, server_message_at_client))
weight_denom = client_outputs.client_weight
round_model_delta = tff.federated_mean(client_outputs.weights_delta,
weight=weight_denom)
server_state = tff.federated_map(server_update_fn,
(server_state, round_model_delta))
aggregated_outputs = metrics_aggregation_computation(
client_outputs.model_output)
return server_state, aggregated_outputs
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.data.Dataset` with placement
`tff.CLIENTS`.
Returns:
A tuple of updated `ServerState` and `tf.Tensor` of average loss.
"""
server_message = tff.federated_map(server_message_fn, server_state)
server_message_at_client = tff.federated_broadcast(server_message)
client_outputs = tff.federated_map(
client_update_fn, (federated_dataset, server_message_at_client))
# Model deltas are equally weighted in DP.
round_model_delta = tff.federated_mean(client_outputs.weights_delta)
server_state = tff.federated_map(server_update_fn,
(server_state, round_model_delta))
round_loss_metric = tff.federated_mean(client_outputs.model_output)
return server_state, round_loss_metric
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)
round_grads_norm = tff.federated_mean(
client_outputs.optimizer_output.flat_grads_norm_sum,
weight=weight_denom)
server_state = tff.federated_map(
server_update_fn,
(server_state, round_model_delta, round_grads_norm))
aggregated_outputs = dummy_model_for_metadata.federated_output_computation(
client_outputs.model_output)
if isinstance(aggregated_outputs.type_signature, tff.StructType):
aggregated_outputs = tff.federated_zip(aggregated_outputs)
return server_state, aggregated_outputs
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, federated_dataset, client_states):
"""Orchestration logic for one round of computation.
Args:
server_state: A `stateful_fedavg_tf.ServerState`.
federated_dataset: A federated `tf.data.Dataset` with placement
`tff.CLIENTS`.
client_states: A federated `stateful_fedavg_tf.ClientState`.
Returns:
A tuple of updated `ServerState` and `tf.Tensor` of average loss.
"""
server_message = tff.federated_map(server_message_fn, server_state)
server_message_at_client = tff.federated_broadcast(server_message)
client_outputs = tff.federated_map(
client_update_fn,
(federated_dataset, client_states, server_message_at_client))
weight_denom = client_outputs.client_weight
round_model_delta = tff.federated_mean(client_outputs.weights_delta,
weight=weight_denom)
total_iters_count = tff.federated_sum(
client_outputs.client_state.iters_count)
server_state = tff.federated_map(
server_update_fn,
(server_state, round_model_delta, total_iters_count))
round_loss_metric = tff.federated_mean(client_outputs.model_output,
weight=weight_denom)
return server_state, round_loss_metric, client_outputs.client_state
def validate(weights, datasets, client_states):
broadcast = tff.federated_broadcast(weights)
outputs = tff.federated_map(validate_client_tf,
(datasets, client_states, broadcast))
metrics = model.federated_output_computation(outputs.metrics)
return metrics
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_optimizer_state = tff.federated_broadcast(
server_state.client_optimizer_state)
client_round_num = tff.federated_broadcast(server_state.round_num)
client_outputs = tff.federated_map(
client_update_fn, (federated_dataset, client_model,
client_optimizer_state, client_round_num))
client_model_weight = client_outputs.client_weight.model_weight
client_opt_weight = client_outputs.client_weight.optimizer_weight
model_delta = tff.federated_mean(
client_outputs.weights_delta, weight=client_model_weight)
# We convert the optimizer state to a float type so that it can be used
# with thing such as `tff.federated_mean`. This is only necessary because
# `tf.keras.Optimizer` objects have a state with an integer indicating
# the number of times it has been applied.
client_optimizer_state_delta = tff.federated_map(
_convert_opt_state_to_float, client_outputs.optimizer_state_delta)
client_optimizer_state_delta = optimizer_aggregator(
client_optimizer_state_delta, weight=client_opt_weight)
# We conver the optimizer state back into one with an integer round number
client_optimizer_state_delta = tff.federated_map(
_convert_opt_state_to_int, client_optimizer_state_delta)
server_state = tff.federated_map(
server_update_fn,
(server_state, model_delta, client_optimizer_state_delta))
aggregated_outputs = placeholder_model.federated_output_computation(
client_outputs.model_output)
if aggregated_outputs.type_signature.is_struct():
aggregated_outputs = tff.federated_zip(aggregated_outputs)
return server_state, aggregated_outputs
def train_one_round(model, federated_data):
locally_trained_models = tff.federated_map(
train_on_one_client,
collections.OrderedDict([('model', tff.federated_broadcast(model)),
('batches', federated_data)]))
return tff.federated_aggregate(locally_trained_models,
make_zero_model_and_count(), accumulate,
merge, report)
def comp(temperatures, threshold):
return tff.federated_mean(
tff.federated_map(
count_over,
tff.federated_zip(
[temperatures,
tff.federated_broadcast(threshold)])),
tff.federated_map(count_total, temperatures))
def robust_aggregation_fn(value, weight):
aggregate = tff.federated_mean(value, weight=weight)
for _ in range(num_communication_passes - 1):
aggregate_at_client = tff.federated_broadcast(aggregate)
updated_weight = tff.federated_map(
update_weight_fn, (weight, aggregate_at_client, value))
aggregate = tff.federated_mean(value, weight=updated_weight)
return aggregate
def _robust_aggregation_fn(state, value, weight):
aggregate = tff.federated_mean(value, weight=weight)
for _ in range(num_communication_passes - 1):
aggregate_at_client = tff.federated_broadcast(aggregate)
updated_weight = tff.federated_map(
update_weight_fn, (weight, aggregate_at_client, value))
aggregate = tff.federated_mean(value, weight=updated_weight)
no_metrics = tff.federated_value((), tff.SERVER)
return tff.templates.MeasuredProcessOutput(state, aggregate,
no_metrics)
def evaluate(weights, datasets, client_states):
broadcast = tff.federated_broadcast(weights)
outputs = tff.federated_map(evaluate_client_tf,
(datasets, client_states, broadcast))
confusion_matrix = tff.federated_sum(outputs.confusion_matrix)
aggregated_metrics = model.federated_output_computation(
outputs.metrics)
collected_metrics = tff.federated_collect(outputs.metrics)
return confusion_matrix, aggregated_metrics, collected_metrics
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 aggregated metrics.
"""
client_model = tff.federated_broadcast(server_state.model)
client_round_number = tff.federated_broadcast(server_state.round_num)
client_outputs = tff.federated_map(
client_update_fn,
(federated_dataset, client_model, client_round_number))
if len(aggregation_process.next.type_signature.parameter) == 3:
# Weighted aggregation.
aggregation_output = aggregation_process.next(
server_state.aggregator_state,
client_outputs.weights_delta,
weight=client_outputs.client_weight)
else:
# Unweighted aggregation.
aggregation_output = aggregation_process.next(
server_state.aggregator_state, client_outputs.weights_delta)
round_model_delta = aggregation_output.result
server_state = tff.federated_map(
server_update_fn,
(server_state, round_model_delta, aggregation_output.state))
aggregated_model_outputs = federated_output_computation(
client_outputs.model_output)
# We drop the `measurements` portion of the aggregation_output here, as it
# is not necessary for our experiments.
return server_state, aggregated_model_outputs
def next_fn(server_weights, federated_dataset):
# Broadcast the server weights to the clients.
server_weights_at_client = tff.federated_broadcast(server_weights)
# Each client computes their updated weights.
client_weights = client_update(federated_dataset, server_weights_at_client)
# The server averages these updates.
mean_client_weights = np.mean(client_weights)
# The server updates its model.
server_weights = server_update(mean_client_weights)
return server_weights
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
def run_one_round(server_state, federated_dataset, malicious_dataset,
malicious_clients):
"""Orchestration logic for one round of computation.
Args:
server_state: A `ServerState`.
federated_dataset: A federated `tf.Dataset` with placement `tff.CLIENTS`.
malicious_dataset: A federated `tf.Dataset` with placement `tff.CLIENTS`.
consisting of malicious datasets.
malicious_clients: A federated `tf.bool` 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, malicious_dataset,
malicious_clients, client_model))
weight_denom = client_outputs.weights_delta_weight
# If the aggregation process' next function takes three arguments it is
# weighted, otherwise, unweighted. Unfortunately there is no better way
# to determine this.
if len(aggregation_process.next.type_signature.parameter) == 3:
aggregate_output = aggregation_process.next(
server_state.delta_aggregate_state,
client_outputs.weights_delta,
weight=weight_denom)
else:
aggregate_output = aggregation_process.next(
server_state.delta_aggregate_state,
client_outputs.weights_delta)
new_delta_aggregate_state = aggregate_output.state
round_model_delta = aggregate_output.result
server_state = tff.federated_map(
server_update_fn,
(server_state, round_model_delta, new_delta_aggregate_state))
aggregated_outputs = dummy_model_for_metadata.federated_output_computation(
client_outputs.model_output)
if isinstance(aggregated_outputs.type_signature, tff.StructType):
aggregated_outputs = tff.federated_zip(aggregated_outputs)
return server_state, aggregated_outputs
def next_tff(server_state, datasets, client_states):
message = tff.federated_map(state_to_message_tf, server_state)
broadcast = tff.federated_broadcast(message)
outputs = tff.federated_map(update_client_tf,
(datasets, client_states, broadcast))
weights_delta = tff.federated_mean(outputs.weights_delta,
weight=outputs.client_weight)
metrics = model.federated_output_computation(outputs.metrics)
next_state = tff.federated_map(update_server_tf,
(server_state, weights_delta))
return next_state, metrics, outputs.client_state
def run_gradient_computation_round(server_state, federated_dataset):
"""Orchestration logic for one round of gradient computation.
Args:
server_state: A `ServerState`.
federated_dataset: A federated `tf.data.Dataset` with placement
`tff.CLIENTS`.
Returns:
A tuple of updated `tf.Tensor` of clients initial probability and `ClientOutput`.
"""
server_message = tff.federated_map(server_message_fn, server_state)
server_message_at_client = tff.federated_broadcast(server_message)
client_outputs = tff.federated_map(
client_update_fn,
(federated_dataset, server_message_at_client))
update_norm_sum_weighted = tff.federated_sum(
client_outputs.update_norm_weighted)
norm_sum_clients_weighted = tff.federated_broadcast(
update_norm_sum_weighted)
prob_init = scale_on_clients(client_outputs.update_norm_weighted,
norm_sum_clients_weighted)
return prob_init, client_outputs
def next_fn(state, value):
clip_range_lower, clip_range_upper = self._get_clip_range()
# Modular clip values before aggregation.
clipped_value = tff.federated_map(
modular_clip_by_value_tff,
(value, tff.federated_broadcast(clip_range_lower),
tff.federated_broadcast(clip_range_upper)))
(agg_output_state, agg_output_result,
agg_output_measurements) = inner_agg_next(state, clipped_value)
# Clip the aggregate to the same range again (not considering summands).
clipped_agg_output_result = tff.federated_map(
modular_clip_by_value_tff,
(agg_output_result, clip_range_lower, clip_range_upper))
measurements = collections.OrderedDict(
agg_process=agg_output_measurements)
return tff.templates.MeasuredProcessOutput(
state=agg_output_state,
result=clipped_agg_output_result,
measurements=tff.federated_zip(measurements))
def next_fn(server_weights, federated_dataset):
# 将服务器模型广播到客户端上
server_weights_at_client = tff.federated_broadcast(server_weights)
# 客户端计算更新过程，并更新参数
client_weights, clients_loss = tff.federated_map(
client_update_fn, (federated_dataset, server_weights_at_client))
# 服务器平均所有客户端更新的模型参数
mean_client_weights = tff.federated_mean(client_weights)
# 服务器更新他的模型
server_weights = tff.federated_map(server_update_fn, mean_client_weights)
return server_weights, client_weights, clients_loss
def run_one_round(server_state, server_gen_inputs, client_gen_inputs,
client_real_data):
"""The `tff.Computation` to be returned."""
# TODO(b/131429028): The federated_zip should be automatic.
from_server = tff.federated_zip(
gan_training_tf_fns.FromServer(
generator_weights=server_state.generator_weights,
discriminator_weights=server_state.discriminator_weights))
client_input = tff.federated_broadcast(from_server)
client_outputs = tff.federated_map(
client_computation,
(client_gen_inputs, client_real_data, client_input))
if gan.dp_averaging_fn is None:
# Not using differential privacy.
new_dp_averaging_state = server_state.dp_averaging_state
averaged_discriminator_weights_delta = tff.federated_mean(
client_outputs.discriminator_weights_delta,
weight=client_outputs.update_weight)
else:
# Using differential privacy. Note that the weight argument is set to None
# here. This is because the DP aggregation code explicitly does not do
# weighted aggregation. (If weighted aggregation is desired, differential
# privacy needs to be turned off.)
new_dp_averaging_state, averaged_discriminator_weights_delta = (
gan.dp_averaging_fn(server_state.dp_averaging_state,
client_outputs.discriminator_weights_delta,
weight=None))
# TODO(b/131085687): Perhaps reconsider the choice to also use
# ClientOutput to hold the aggregated client output.
aggregated_client_output = gan_training_tf_fns.ClientOutput(
discriminator_weights_delta=averaged_discriminator_weights_delta,
# We don't actually need the aggregated update_weight, but
# this keeps the types of the non-aggregated and aggregated
# client_output the same, which is convenient. And I can
# imagine wanting this.
update_weight=tff.federated_sum(client_outputs.update_weight),
counters=tff.federated_sum(client_outputs.counters))
# TODO(b/131839522): This federated_zip shouldn't be needed.
aggregated_client_output = tff.federated_zip(aggregated_client_output)
server_state = tff.federated_map(
server_computation,
(server_state, server_gen_inputs, aggregated_client_output,
new_dp_averaging_state))
return server_state