def next_fn(state, value):
encode_params, decode_before_sum_params, decode_after_sum_params = (
intrinsics.federated_map(get_params_fn, state))
encode_params = intrinsics.federated_broadcast(encode_params)
decode_before_sum_params = intrinsics.federated_broadcast(
decode_before_sum_params)
encoded_values = intrinsics.federated_map(
encode_fn, [value, encode_params, decode_before_sum_params])
aggregated_values = intrinsics.federated_aggregate(
encoded_values, zero_fn(), accumulate_fn, merge_fn, report_fn)
decoded_values = intrinsics.federated_map(
decode_after_sum_fn,
[aggregated_values.values, decode_after_sum_params])
updated_state = intrinsics.federated_map(
update_state_fn, [state, aggregated_values.state_update_tensors])
empty_metrics = intrinsics.federated_value((), placements.SERVER)
return measured_process.MeasuredProcessOutput(
state=updated_state,
result=decoded_values,
measurements=empty_metrics)
def next_fn(state, value):
server_scale_factor = state['scale_factor']
client_scale_factor = intrinsics.federated_broadcast(
server_scale_factor)
server_prior_norm_bound = state['prior_norm_bound']
prior_norm_bound = intrinsics.federated_broadcast(
server_prior_norm_bound)
discretized_value = intrinsics.federated_map(
discretize_fn, (value, client_scale_factor, prior_norm_bound))
inner_state = state['inner_agg_process']
inner_agg_output = inner_agg_process.next(inner_state,
discretized_value)
undiscretized_agg_value = intrinsics.federated_map(
undiscretize_fn,
(inner_agg_output.result, server_scale_factor))
new_state = collections.OrderedDict(
scale_factor=server_scale_factor,
prior_norm_bound=server_prior_norm_bound,
inner_agg_process=inner_agg_output.state)
measurements = collections.OrderedDict(
discretize=inner_agg_output.measurements)
return measured_process.MeasuredProcessOutput(
state=intrinsics.federated_zip(new_state),
result=undiscretized_agg_value,
measurements=intrinsics.federated_zip(measurements))
def next_fn(state, value):
clip_lower = intrinsics.federated_value(self._clip_range_lower,
placements.SERVER)
clip_upper = intrinsics.federated_value(self._clip_range_upper,
placements.SERVER)
# Modular clip values before aggregation.
clipped_value = intrinsics.federated_map(
modular_clip_by_value_fn,
(value, intrinsics.federated_broadcast(clip_lower),
intrinsics.federated_broadcast(clip_upper)))
inner_agg_output = inner_agg_next(state, clipped_value)
# Clip the aggregate to the same range again (not considering summands).
clipped_agg_output_result = intrinsics.federated_map(
modular_clip_by_value_fn,
(inner_agg_output.result, clip_lower, clip_upper))
measurements = collections.OrderedDict(
modclip=inner_agg_output.measurements)
if self._estimate_stddev:
estimate = intrinsics.federated_map(
estimator_fn,
(clipped_agg_output_result, clip_lower, clip_upper))
measurements['estimated_stddev'] = estimate
return measured_process.MeasuredProcessOutput(
state=inner_agg_output.state,
result=clipped_agg_output_result,
measurements=intrinsics.federated_zip(measurements))
def _sum_securely(self, value, upper_bound, lower_bound):
"""Securely sums `value` placed at CLIENTS."""
if self._config_mode == _Config.INT:
value = intrinsics.federated_map(
_client_shift, (value, intrinsics.federated_broadcast(upper_bound),
intrinsics.federated_broadcast(lower_bound)))
value = intrinsics.federated_secure_sum(value, self._secagg_bitwidth)
num_summands = intrinsics.federated_sum(_client_one())
value = intrinsics.federated_map(_server_shift,
(value, lower_bound, num_summands))
return value
elif self._config_mode == _Config.FLOAT:
return primitives.secure_quantized_sum(value, lower_bound, upper_bound)
else:
raise ValueError(f'Unexpected internal config type: {self._config_mode}')
def next_fn_impl(state, value, clip_fn, inner_agg_process, weight=None):
clipping_norm_state, agg_state, clipped_count_state = state
clipping_norm = clipping_norm_process.report(clipping_norm_state)
clipped_value, global_norm, was_clipped = intrinsics.federated_map(
clip_fn, (value, intrinsics.federated_broadcast(clipping_norm)))
new_clipping_norm_state = clipping_norm_process.next(
clipping_norm_state, global_norm)
if weight is None:
agg_output = inner_agg_process.next(agg_state, clipped_value)
else:
agg_output = inner_agg_process.next(agg_state, clipped_value,
weight)
clipped_count_output = clipped_count_agg_process.next(
clipped_count_state, was_clipped)
new_state = collections.OrderedDict([
(prefix('ing_norm'), new_clipping_norm_state),
('inner_agg', agg_output.state),
(prefix('ed_count_agg'), clipped_count_output.state)
])
measurements = collections.OrderedDict([
(prefix('ing'), agg_output.measurements),
(prefix('ing_norm'), clipping_norm),
(prefix('ed_count'), clipped_count_output.result)
])
return measured_process.MeasuredProcessOutput(
state=intrinsics.federated_zip(new_state),
result=agg_output.result,
measurements=intrinsics.federated_zip(measurements))
def add_server_number_plus_one(server_number, client_numbers):
one = intrinsics.federated_value(1, placements.SERVER)
server_context = intrinsics.federated_map(add,
(one, server_number))
client_context = intrinsics.federated_broadcast(server_context)
return intrinsics.federated_map(add,
(client_context, client_numbers))
def _train_one_round(model, federated_data):
locally_trained_models = intrinsics.federated_map(
_train_on_one_client,
collections.OrderedDict([('model',
intrinsics.federated_broadcast(model)),
('batches', federated_data)]))
return intrinsics.federated_mean(locally_trained_models)
def stateful_broadcast(state, value):
test_metrics = intrinsics.federated_value(
3.0, placements.SERVER)
return measured_process_lib.MeasuredProcessOutput(
state=state,
result=intrinsics.federated_broadcast(value),
measurements=test_metrics)
def next_fn(state, value):
server_step_size = state['step_size']
client_step_size = intrinsics.federated_broadcast(server_step_size)
discretized_value = intrinsics.federated_map(discretize_fn,
(value, client_step_size))
inner_state = state['inner_agg_process']
inner_agg_output = inner_agg_process.next(inner_state, discretized_value)
undiscretized_agg_value = intrinsics.federated_map(
undiscretize_fn, (inner_agg_output.result, server_step_size))
new_state = collections.OrderedDict(
step_size=server_step_size, inner_agg_process=inner_agg_output.state)
measurements = collections.OrderedDict(
deterministic_discretization=inner_agg_output.measurements)
if self._distortion_aggregation_factory is not None:
distortions = intrinsics.federated_map(distortion_measurement_fn,
(value, client_step_size))
aggregate_distortion = distortion_aggregation_process.next(
distortion_aggregation_process.initialize(), distortions).result
measurements['distortion'] = aggregate_distortion
return measured_process.MeasuredProcessOutput(
state=intrinsics.federated_zip(new_state),
result=undiscretized_agg_value,
measurements=intrinsics.federated_zip(measurements))
def comp(temperatures, threshold):
return intrinsics.federated_mean(
intrinsics.federated_map(
count_over,
intrinsics.federated_zip([
temperatures,
intrinsics.federated_broadcast(threshold)
])), intrinsics.federated_map(count_total, temperatures))
def encoded_broadcast_comp(state, value):
"""Encoded broadcast federated_computation."""
empty_metrics = intrinsics.federated_value((), placements.SERVER)
new_state, encoded_value = intrinsics.federated_map(encode_fn,
(state, value))
client_encoded_value = intrinsics.federated_broadcast(encoded_value)
client_value = intrinsics.federated_map(decode_fn, client_encoded_value)
return measured_process.MeasuredProcessOutput(
state=new_state, result=client_value, measurements=empty_metrics)
def next_comp(state, value):
return measured_process.MeasuredProcessOutput(
state=intrinsics.federated_map(_add_one, state),
result=intrinsics.federated_broadcast(value),
# Arbitrary metrics for testing.
measurements=intrinsics.federated_map(
tensorflow_computation.tf_computation(
lambda v: tf.linalg.global_norm(tf.nest.flatten(v)) + 3.0),
value))
def computation(arg):
server_data, client_data = arg
context_at_server = intrinsics.federated_map(bf.compute_server_context,
server_data)
context_at_clients = intrinsics.federated_broadcast(context_at_server)
client_processing_arg = intrinsics.federated_zip(
(context_at_clients, client_data))
return intrinsics.federated_map(bf.client_processing,
client_processing_arg)
def next_fn(server_state, client_val):
"""`next` function for `tff.templates.IterativeProcess`."""
server_update = intrinsics.federated_sum(client_val)
server_output = intrinsics.federated_value((), placements.SERVER)
state_at_clients = intrinsics.federated_broadcast(server_state)
lambda_returning_sum = computation_returning_lambda()
sum_fn = lambda_returning_sum(1)
server_output = sum_fn(state_at_clients)
return server_update, server_output
def next_fn(state, weights, client_data):
round_num_at_clients = intrinsics.federated_broadcast(state)
client_result, model_outputs = intrinsics.federated_map(
client_update_computation, (weights, client_data, round_num_at_clients))
updated_state = intrinsics.federated_map(add_one, state)
train_metrics = metrics_aggregation_fn(model_outputs)
measurements = intrinsics.federated_zip(
collections.OrderedDict(train=train_metrics))
return measured_process.MeasuredProcessOutput(updated_state, client_result,
measurements)
def aggregation_comp(server_arg, client_arg):
client_sums = intrinsics.federated_map(compute_tuple_sum, client_arg)
summed_client_value = intrinsics.federated_sum(client_sums)
broadcast_sum = intrinsics.federated_broadcast(summed_client_value)
# Adding a function call here requires normalization into CDF before
# checking the aggregation-dependence condition.
client_tuple = package_args_as_tuple(client_sums, broadcast_sum)
summed_client_value = intrinsics.federated_sum(client_tuple[0])
return intrinsics.federated_map(compute_sum,
(server_arg, summed_client_value))
def next_comp(server_state, client_data): del server_state, client_data client_val = intrinsics.federated_value(0, placements.CLIENTS) server_agg = intrinsics.federated_sum(client_val) # This broadcast is dependent on the result of the above aggregation, # which is not supported by MapReduce form. broadcasted = intrinsics.federated_broadcast(server_agg) server_agg_again = intrinsics.federated_sum(broadcasted) # `next` must return two values. return server_agg_again, intrinsics.federated_value((), placements.SERVER)
def next_fn(server_state, client_data):
"""The `next` function for `tff.templates.IterativeProcess`."""
s2 = intrinsics.federated_map(prepare, server_state)
client_input = intrinsics.federated_broadcast(s2)
c3 = intrinsics.federated_zip([client_data, client_input])
client_updates = intrinsics.federated_map(work, c3)
unsecure_update = intrinsics.federated_sum(client_updates)
# No call to `federated_secure_sum_bitwidth`.
s6 = intrinsics.federated_zip([server_state, unsecure_update])
new_server_state, server_output = intrinsics.federated_map(update, s6)
return new_server_state, server_output
def _compute_measurements(self, upper_bound, lower_bound, value_max,
value_min):
"""Creates measurements to be reported. All values are summed securely."""
is_max_clipped = intrinsics.federated_map(
computations.tf_computation(
lambda bound, value: tf.cast(bound < value, COUNT_TF_TYPE)),
(intrinsics.federated_broadcast(upper_bound), value_max))
max_clipped_count = intrinsics.federated_secure_sum(is_max_clipped,
bitwidth=1)
is_min_clipped = intrinsics.federated_map(
computations.tf_computation(
lambda bound, value: tf.cast(bound > value, COUNT_TF_TYPE)),
(intrinsics.federated_broadcast(lower_bound), value_min))
min_clipped_count = intrinsics.federated_secure_sum(is_min_clipped,
bitwidth=1)
measurements = collections.OrderedDict(
secure_upper_clipped_count=max_clipped_count,
secure_lower_clipped_count=min_clipped_count,
secure_upper_threshold=upper_bound,
secure_lower_threshold=lower_bound)
return intrinsics.federated_zip(measurements)
def next_fn(server_state, client_val):
"""`next` function for `tff.templates.IterativeProcess`."""
server_update = intrinsics.federated_zip(
collections.OrderedDict(
num_clients=count_clients_federated(client_val)))
server_output = intrinsics.federated_value((), placements.SERVER)
server_output = intrinsics.federated_sum(
_bind_tf_function(intrinsics.federated_broadcast(server_state),
tf.timestamp))
return server_update, server_output
def next_fn(server_state, client_data):
"""The `next` function for `tff.templates.IterativeProcess`."""
s2 = intrinsics.federated_map(prepare, server_state)
unused_client_input, to_broadcast = broadcast_and_return_arg_and_result(s2)
client_input = intrinsics.federated_broadcast(to_broadcast)
c3 = intrinsics.federated_zip([client_data, client_input])
client_updates = intrinsics.federated_map(work, c3)
unsecure_update = intrinsics.federated_sum(client_updates[0])
secure_update = intrinsics.federated_secure_sum_bitwidth(
client_updates[1], 8)
s6 = intrinsics.federated_zip(
[server_state, [unsecure_update, secure_update]])
new_server_state, server_output = intrinsics.federated_map(update, s6)
return new_server_state, server_output
def next_fn(server_state, client_data):
"""The `next` function for `tff.templates.IterativeProcess`."""
s2 = intrinsics.federated_map(prepare, server_state)
client_input = intrinsics.federated_broadcast(s2)
c3 = intrinsics.federated_zip([client_data, client_input])
client_updates = intrinsics.federated_map(work, c3)
unsecure_update = intrinsics.federated_sum(client_updates[0])
secure_update = intrinsics.federated_secure_sum_bitwidth(
client_updates[1], 8)
new_server_state = intrinsics.federated_zip(
[unsecure_update, secure_update])
# No call to `federated_map` with an `update` function.
server_output = intrinsics.federated_value([], placements.SERVER)
return new_server_state, server_output
def personalization_eval(server_model_weights, federated_client_input):
"""TFF orchestration logic."""
client_init_weights = intrinsics.federated_broadcast(server_model_weights)
client_final_metrics = intrinsics.federated_map(
_client_computation, (client_init_weights, federated_client_input))
# WARNING: Collecting information from clients can be risky. Users have to
# make sure that it is proper to collect those metrics from clients.
# TODO(b/147889283): Add a link to the TFF doc once it exists.
sampling_output = aggregation_process.next(
aggregation_process.initialize(), # No state.
client_final_metrics)
# In the future we may want to output `sampling_output.measurements` also
# but currently it is empty.
return sampling_output.result
def server_eval(server_model_weights, federated_dataset):
if broadcast_process is not None:
# TODO(b/179091838): Confirm that the process has no state.
# TODO(b/179091838): Zip the measurements from the broadcast_process with
# the result of `model.federated_output_computation` below to avoid
# dropping these metrics.
broadcast_output = broadcast_process.next(
broadcast_process.initialize(), server_model_weights)
client_outputs = intrinsics.federated_map(
client_eval, (broadcast_output.result, federated_dataset))
else:
client_outputs = intrinsics.federated_map(client_eval, [
intrinsics.federated_broadcast(server_model_weights),
federated_dataset
])
return model.federated_output_computation(client_outputs.local_outputs)
def next_fn(state, value):
quantile_query_state, agg_state = state
params = intrinsics.federated_broadcast(
intrinsics.federated_map(derive_sample_params,
quantile_query_state))
quantile_record = intrinsics.federated_map(get_quantile_record,
(params, value))
quantile_agg_output = quantile_agg_process.next(
agg_state, quantile_record)
_, new_quantile_query_state, _ = intrinsics.federated_map(
get_noised_result,
(quantile_agg_output.result, quantile_query_state))
return intrinsics.federated_zip(
(new_quantile_query_state, quantile_agg_output.state))
def next_fn(state, value):
quantile_query_state, agg_state = state
params = intrinsics.federated_broadcast(
intrinsics.federated_map(derive_sample_params, quantile_query_state))
quantile_record = intrinsics.federated_map(get_quantile_record,
(params, value))
(new_agg_state, agg_result,
agg_measurements) = quantile_agg_process.next(agg_state, quantile_record)
# We expect the quantile record aggregation process to be something simple
# like basic sum, so we won't surface its measurements.
del agg_measurements
_, new_quantile_query_state = intrinsics.federated_map(
get_noised_result, (agg_result, quantile_query_state))
return intrinsics.federated_zip((new_quantile_query_state, new_agg_state))
def next_computation(arg):
"""The logic of a single MapReduce processing round."""
s1 = arg[0]
c1 = arg[1]
s2 = intrinsics.federated_map(mrf.prepare, s1)
c2 = intrinsics.federated_broadcast(s2)
c3 = intrinsics.federated_zip([c1, c2])
c4 = intrinsics.federated_map(mrf.work, c3)
c5 = c4[0]
c6 = c4[1]
s3 = intrinsics.federated_aggregate(c5, mrf.zero(), mrf.accumulate,
mrf.merge, mrf.report)
s4 = intrinsics.federated_secure_sum_bitwidth(c6, mrf.bitwidth())
s5 = intrinsics.federated_zip([s3, s4])
s6 = intrinsics.federated_zip([s1, s5])
s7 = intrinsics.federated_map(mrf.update, s6)
s8 = s7[0]
s9 = s7[1]
return s8, s9
def next_fn_impl(state, value):
inner_state, my_state = state
client_my_state = intrinsics.federated_broadcast(my_state)
projected_value = intrinsics.federated_map(client_transform,
(value, client_my_state))
inner_agg_output = inner_agg_process.next(inner_state, projected_value)
aggregate_value = intrinsics.federated_map(
server_transform, (inner_agg_output.result, my_state))
new_state = (inner_agg_output.state,
intrinsics.federated_map(update_my_state, my_state))
measurements = collections.OrderedDict([(name,
inner_agg_output.measurements)])
return measured_process.MeasuredProcessOutput(
state=intrinsics.federated_zip(new_state),
result=aggregate_value,
measurements=intrinsics.federated_zip(measurements))
def server_eval(server_model_weights, federated_dataset):
client_eval = build_local_evaluation(model_fn, model_weights_type,
batch_type,
use_experimental_simulation_loop)
if broadcast_process is not None:
# TODO(b/179091838): Zip the measurements from the broadcast_process with
# the result of `model_metrics` below to avoid dropping these metrics.
broadcast_output = broadcast_process.next(
broadcast_process.initialize(), server_model_weights)
client_outputs = intrinsics.federated_map(
client_eval, (broadcast_output.result, federated_dataset))
else:
client_outputs = intrinsics.federated_map(client_eval, [
intrinsics.federated_broadcast(server_model_weights),
federated_dataset
])
model_metrics = metrics_aggregation_computation(
client_outputs.local_outputs)
return intrinsics.federated_zip(
collections.OrderedDict(eval=model_metrics))
def server_eval(server_model_weights, federated_dataset):
if broadcast_process is not None:
# TODO(b/179091838): Zip the measurements from the broadcast_process with
# the result of `model.federated_output_computation` below to avoid
# dropping these metrics.
broadcast_output = broadcast_process.next(
broadcast_process.initialize(), server_model_weights)
client_outputs = intrinsics.federated_map(
client_eval, (broadcast_output.result, federated_dataset))
else:
client_outputs = intrinsics.federated_map(client_eval, [
intrinsics.federated_broadcast(server_model_weights),
federated_dataset
])
model_metrics = model.federated_output_computation(
client_outputs.local_outputs)
statistics = collections.OrderedDict(
num_examples=intrinsics.federated_sum(client_outputs.num_examples))
return intrinsics.federated_zip(
collections.OrderedDict(eval=model_metrics, stat=statistics))
