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, 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
def server_init():
initial_model, server_optimizer_state = tff.federated_eval(
server_init_tf, tff.SERVER)
return tff.federated_zip(
ServerState(model=initial_model,
optimizer_state=server_optimizer_state,
delta_aggregate_state=aggregation_process_init()))
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):
"""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 next_fn(state, deltas, weights):
@tff.tf_computation(model_update_type)
def clip_by_global_norm(update):
clipped_update, global_norm = tf.clip_by_global_norm(
tf.nest.flatten(update), tf.constant(clip_norm))
was_clipped = tf.cond(
tf.greater(global_norm, tf.constant(clip_norm)),
lambda: tf.constant(1),
lambda: tf.constant(0),
)
clipped_update = tf.nest.pack_sequence_as(update, clipped_update)
return clipped_update, global_norm, was_clipped
clipped_deltas, client_norms, client_was_clipped = tff.federated_map(
clip_by_global_norm, deltas)
return collections.OrderedDict(
state=state,
result=tff.federated_mean(clipped_deltas, weight=weights),
measurements=tff.federated_zip(
NormClippedAggregationMetrics(
max_global_norm=tff.utils.federated_max(client_norms),
num_clipped=tff.federated_sum(client_was_clipped),
)))
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,
tff.federated_zip([federated_dataset, discovered_prefixes, round_num]))
accumulated_votes = tff.federated_sum(client_outputs.client_votes)
server_state = tff.federated_map(server_update_fn,
(server_state, accumulated_votes))
server_output = tff.federated_value([], tff.SERVER)
return server_state, server_output
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 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 fed_server_initial_state():
state = tff.federated_eval(build_server_initial_state_comp(gan), tff.SERVER)
server_initial_state = tff.federated_zip(
gan_training_tf_fns.ServerState(
state.generator_weights,
state.discriminator_weights,
state.counters,
aggregation_state=gan.aggregation_process.initialize()))
return server_initial_state
def server_init_tff():
"""Returns a `reconstruction_utils.ServerState` placed at `tff.SERVER`."""
tf_init_tuple = tff.federated_eval(server_init_tf, tff.SERVER)
aggregation_process_init = aggregation_process.initialize()
return tff.federated_zip(
reconstruction_utils.ServerState(
model=tf_init_tuple[0],
optimizer_state=tf_init_tuple[1],
round_num=tf_init_tuple[2],
aggregator_state=aggregation_process_init))
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 fed_server_initial_state():
state = tff.federated_eval(build_server_initial_state_comp(gan), tff.SERVER)
dp_averaging_state = (
state.dp_averaging_state
if gan.dp_averaging_fn is None else gan.dp_averaging_fn.initialize())
server_initial_state = tff.federated_zip(
gan_training_tf_fns.ServerState(
state.generator_weights,
state.discriminator_weights,
state.counters,
dp_averaging_state=dp_averaging_state))
return server_initial_state
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, 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 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 next_fn(state, deltas, weights=None):
@tff.tf_computation(deltas.type_signature.member, tf.float32)
def clip_by_global_norm(delta, clip_norm):
clipped, global_norm = tf.clip_by_global_norm(
tf.nest.flatten(delta), clip_norm)
clipped_deltas = tf.nest.pack_sequence_as(delta, clipped)
return clipped_deltas, global_norm
client_clip_norm = tff.federated_broadcast(state.clip_norm)
clipped_deltas, client_norms = tff.federated_map(
clip_by_global_norm, (deltas, client_clip_norm))
# clip_norm no-op update here but could be set using max_norm.
next_state = tff.federated_zip(
ClipNormAggregateState(
clip_norm=state.clip_norm,
max_norm=tff.utils.federated_max(client_norms)))
return next_state, tff.federated_mean(clipped_deltas, weight=weights)
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 aggregation_process.is_weighted:
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_fn(state, deltas, weights=None):
@tff.tf_computation(deltas.type_signature.member, tf.float32)
def clip_by_global_norm(delta, clip_norm):
# TODO(b/123092620): Replace anonymous_tuple with tf.nest.
delta = anonymous_tuple.from_container(delta)
clipped, global_norm = tf.clip_by_global_norm(
anonymous_tuple.flatten(delta), clip_norm)
return anonymous_tuple.pack_sequence_as(delta,
clipped), global_norm
client_clip_norm = tff.federated_broadcast(state.clip_norm)
clipped_deltas, client_norms = tff.federated_map(
clip_by_global_norm, (deltas, client_clip_norm))
# clip_norm no-op update here but could be set using max_norm.
next_state = tff.federated_zip(
ClipNormAggregateState(
clip_norm=state.clip_norm,
max_norm=tff.utils.federated_max(client_norms)))
return next_state, tff.federated_mean(clipped_deltas, weight=weights)
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))
client_weight = client_outputs.client_weight
model_delta = tff.federated_mean(client_outputs.weights_delta,
weight=client_weight)
global_cor_states = tff.federated_mean(client_outputs.local_cor_states,
weight=client_weight)
if correction == 'joint':
server_state = tff.federated_map(
server_update_joint_cor_fn,
(server_state, model_delta, global_cor_states))
elif correction == 'local':
server_state = tff.federated_map(server_update_fn,
(server_state, model_delta))
else:
raise TypeError('Correction method must be local or joint.')
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 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 foo(x): return tff.federated_zip(x)
def mean_over_threshold(temperatures, threshold):
client_data = tff.federated_broadcast(threshold)
client_data = tff.federated_zip([temperatures, client_data])
result_map = tff.federated_map(count_over, client_data)
count_map = tff.federated_map(count_total, temperatures)
return tff.federated_mean(result_map, count_map)
def run_one_round(server_state, federated_dataset, ids):
"""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, ids))
client_weight = client_outputs.client_weight
client_id = client_outputs.client_id
#LOSS SELECTION:
# losses_at_server = tff.federated_collect(client_outputs.model_output)
# weights_at_server = tff.federated_collect(client_weight)
@computations.tf_computation
def zeros_fn():
return tf.zeros(shape=[total_clients, 1], dtype=tf.float32)
zero = zeros_fn()
at_server_type = tff.TensorType(shape=[total_clients, 1],
dtype=tf.float32)
# list_type = tff.SequenceType( tff.TensorType(dtype=tf.float32))
client_output_type = client_update_fn.type_signature.result
@computations.tf_computation(at_server_type, client_output_type)
def accumulate_weight(u, t):
value = t.client_weight
index = t.client_id
new_u = tf.tensor_scatter_nd_update(u, index, value)
return new_u
@computations.tf_computation(at_server_type, client_output_type)
def accumulate_loss(u, t):
value = tf.reshape(tf.math.reduce_sum(t.model_output['loss']),
shape=[1, 1])
index = t.client_id
new_u = tf.tensor_scatter_nd_update(u, index, value)
return new_u
# output_at_server= tff.federated_collect(client_outputs)
weights_at_server = tff.federated_reduce(client_outputs, zero,
accumulate_weight)
losses_at_server = tff.federated_reduce(client_outputs, zero,
accumulate_loss)
#losses_at_server = tff.federated_aggregate(client_outputs.model_output, zero, accumulate, merge, report)
selected_clients_weights = tff.federated_map(
zero_small_loss_clients, (losses_at_server, weights_at_server,
server_state.effective_num_clients))
# selected_clients_weights_at_client = tff.federated_broadcast(selected_clients_weights)
selected_clients_weights_broadcast = tff.federated_broadcast(
selected_clients_weights)
selected_clients_weights_at_client = tff.federated_map(
select_weight_fn, (selected_clients_weights_broadcast, ids))
aggregation_output = aggregation_process.next(
server_state.delta_aggregate_state, client_outputs.weights_delta,
selected_clients_weights_at_client)
# model_delta = tff.federated_mean(
# client_outputs.weights_delta, weight=client_weight)
server_state = tff.federated_map(
server_update_fn, (server_state, aggregation_output.result))
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
