def foo(temperatures, threshold):
return tff.federated_sum(
tff.federated_map(
tff.tf_computation(
lambda x, y: tf.cast(tf.greater(x, y), tf.int32),
[tf.float32, tf.float32]),
[temperatures, tff.federated_broadcast(threshold)]))
def encoded_mean_fn(state, values, weight):
weighted_values = tff.federated_map(multiply_fn, [values, weight])
updated_state, summed_decoded_values = encoded_sum_fn(
state, weighted_values)
summed_weights = tff.federated_sum(weight)
decoded_values = tff.federated_map(
divide_fn, [summed_decoded_values, summed_weights])
return updated_state, decoded_values
def next_fn(server_state, client_data):
broadcast_state = tff.federated_broadcast(server_state)
@tff.tf_computation(tf.int32, tff.SequenceType(tf.float32))
@tf.function
def some_transform(x, y):
del y # Unused
return x + 1
client_update = tff.federated_map(some_transform,
(broadcast_state, client_data))
aggregate_update = tff.federated_sum(client_update)
server_output = tff.federated_value(1234, tff.SERVER)
return aggregate_update, server_output
def foo(x): return tff.federated_sum(x)
def _(x): return tff.federated_sum(x)