def next_fn(server_state, client_data):
"""The `next` function for `tff.templates.IterativeProcess`."""
del server_state # Unused
# No call to `federated_map` with prepare.
# No call to `federated_broadcast`.
client_updates = intrinsics.federated_map(work, client_data)
unsecure_update = intrinsics.federated_sum(client_updates[0])
secure_update = intrinsics.federated_secure_sum(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 next_fn(server_state, client_data):
"""The `next` function for `tff.utils.IterativeProcess`."""
s2 = intrinsics.federated_map(prepare, server_state)
client_input = intrinsics.federated_broadcast(s2)
c3 = intrinsics.federated_zip([client_data, client_input])
client_updates, client_output = intrinsics.federated_map(work, c3)
unsecure_update = intrinsics.federated_sum(client_updates[0])
secure_update = intrinsics.federated_secure_sum(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, client_output
def next_fn(server_state, client_data):
"""The `next` function for `tff.utils.IterativeProcess`."""
s2 = intrinsics.federated_map(prepare, server_state)
client_input = intrinsics.federated_broadcast(s2)
c3 = intrinsics.federated_zip([client_data, client_input])
# No client output.
client_updates = intrinsics.federated_map(work, c3)
unsecure_update = intrinsics.federated_sum(client_updates[0])
secure_update = intrinsics.federated_secure_sum(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)
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(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_computation(arg):
"""The logic of a single MapReduce processing round."""
s1 = arg[0]
c1 = arg[1]
s2 = intrinsics.federated_map(cf.prepare, s1)
c2 = intrinsics.federated_broadcast(s2)
c3 = intrinsics.federated_zip([c1, c2])
c4 = intrinsics.federated_map(cf.work, c3)
c5 = c4[0]
c6 = c4[1]
s3 = intrinsics.federated_aggregate(c5, cf.zero(), cf.accumulate, cf.merge,
cf.report)
s4 = intrinsics.federated_secure_sum(c6, cf.bitwidth())
s5 = intrinsics.federated_zip([s3, s4])
s6 = intrinsics.federated_zip([s1, s5])
s7 = intrinsics.federated_map(cf.update, s6)
s8 = s7[0]
s9 = s7[1]
return s8, s9
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 federated_aggregations.secure_quantized_sum(
value, lower_bound, upper_bound)
else:
raise ValueError(
f'Unexpected internal config type: {self._config_mode}')
def comp():
ones_at_clients = intrinsics.federated_value(
(1, 1), placement_literals.CLIENTS)
bitwidth = (1, 1)
return intrinsics.federated_secure_sum(ones_at_clients, bitwidth)
def simple_secure_sum():
one_at_clients = intrinsics.federated_value(1, placement_literals.CLIENTS)
bitwidth = 1
return intrinsics.federated_secure_sum(one_at_clients, bitwidth)
def trivial_secure_sum():
empty_at_clients = intrinsics.federated_value((),
placement_literals.CLIENTS)
bitwidth = ()
return intrinsics.federated_secure_sum(empty_at_clients, bitwidth)
def test_raises_type_error_with_bitwith_int_at_server(self):
value = intrinsics.federated_value(1, placement_literals.CLIENTS)
bitwidth = intrinsics.federated_value(1, placement_literals.SERVER)
with self.assertRaises(TypeError):
intrinsics.federated_secure_sum(value, bitwidth)
def secure_quantized_sum(client_value, lower_bound, upper_bound):
"""Quantizes and sums values securely.
Provided `client_value` can be either a Tensor or a nested structure of
Tensors. If it is a nested structure, `lower_bound` and `upper_bound` must be
either both scalars, or both have the same structure as `client_value`, with
each element being a scalar, representing the bounds to be used for each
corresponding Tensor in `client_value`.
This method converts each Tensor in provided `client_value` to appropriate
format and uses the `tff.federated_secure_sum` operator to realize the sum.
The dtype of Tensors in provided `client_value` can be one of `[tf.int32,
tf.int64, tf.float32, tf.float64]`.
If the dtype of `client_value` is `tf.int32` or `tf.int64`, the summation is
possibly exact, depending on `lower_bound` and `upper_bound`: In the case that
`upper_bound - lower_bound < 2**32`, the summation will be exact. If it is
not, `client_value` will be quantized to precision of 32 bits, so the worst
case error introduced for the value of each client will be approximately
`(upper_bound - lower_bound) / 2**32`. Deterministic rounding to nearest value
is used in such cases.
If the dtype of `client_value` is `tf.float32` or `tf.float64`, the summation
is generally *not* accurate up to full floating point precision. Instead, the
values are first clipped to the `[lower_bound, upper_bound]` range. These
values are then uniformly quantized to 32 bit resolution, using deterministic
rounding to round the values to the quantization points. Rounding happens
roughly as follows (implementation is a bit more complex to mitigate numerical
stability issues):
```
values = tf.round(
(client_value - lower_bound) * ((2**32 - 1) / (upper_bound - lower_bound))
```
After summation, the inverse operation if performed, so the return value
is of the same dtype as the input `client_value`.
In terms of accuracy, it is safe to assume accuracy within 7-8 significant
digits for `tf.float32` inputs, and 8-9 significant digits for `tf.float64`
inputs, where the significant digits refer to precision relative to the range
of the provided bounds. Thus, these bounds should not be set extremely wide.
Accuracy losses arise due to (1) quantization within the given clipping range,
(2) float precision of final outputs (e.g. `tf.float32` has 23 bits in its
mantissa), and (3) precision losses that arise in doing math on `tf.float32`
and `tf.float64` inputs.
As a concrete example, if the range is `+/- 1000`, errors up to `1e-4` per
element should be expected for `tf.float32` and up to `1e-5` for `tf.float64`.
Args:
client_value: A `tff.Value` placed at `tff.CLIENTS`.
lower_bound: The smallest possible value for `client_value` (inclusive).
Values smaller than this bound will be clipped. Must be either a scalar or
a nested structure of scalars, matching the structure of `client_value`.
Must be either a Python constant or a `tff.Value` placed at `tff.SERVER`,
with dtype matching that of `client_value`.
upper_bound: The largest possible value for `client_value` (inclusive).
Values greater than this bound will be clipped. Must be either a scalar or
a nested structure of scalars, matching the structure of `client_value`.
Must be either a Python constant or a `tff.Value` placed at `tff.SERVER`,
with dtype matching that of `client_value`.
Returns:
Summed `client_value` placed at `tff.SERVER`, of the same dtype as
`client_value`.
Raises:
TypeError (or its subclasses): If input arguments do not satisfy the type
constraints specified above.
"""
# Possibly converts Python constants to federated values.
client_value, lower_bound, upper_bound = _normalize_secure_quantized_sum_args(
client_value, lower_bound, upper_bound)
# This object is used during decoration of the `client_shift` method, and the
# value stored in this mutable container is used during decoration of the
# `server_shift` method. The reason for this is that we cannot currently get
# the needed information out of `client_value.type_signature.member` as we
# need both the `TensorType` information as well as the Python container
# attached to them.
temp_box = []
# These tf_computations assume the inputs were already validated. In
# particular, that lower_bnd and upper_bnd have the same structure, and if not
# scalar, the structure matches the structure of value.
@computations.tf_computation()
def client_shift(value, lower_bnd, upper_bnd):
assert not temp_box
temp_box.append(tf.nest.map_structure(lambda v: v.dtype, value))
fn = _client_tensor_shift_for_secure_sum
if tf.is_tensor(lower_bnd):
return tf.nest.map_structure(lambda v: fn(v, lower_bnd, upper_bnd), value)
else:
return tf.nest.map_structure(fn, value, lower_bnd, upper_bnd)
@computations.tf_computation()
def server_shift(value, lower_bnd, upper_bnd, summands):
fn = _server_tensor_shift_for_secure_sum
if tf.is_tensor(lower_bnd):
return tf.nest.map_structure(
lambda v, dtype: fn(summands, v, lower_bnd, upper_bnd, dtype), value,
temp_box[0])
else:
return tf.nest.map_structure(lambda *args: fn(summands, *args), value,
lower_bnd, upper_bnd, temp_box[0])
client_one = intrinsics.federated_value(1, placements.CLIENTS)
# Orchestration.
client_lower_bound = intrinsics.federated_broadcast(lower_bound)
client_upper_bound = intrinsics.federated_broadcast(upper_bound)
value = intrinsics.federated_map(
client_shift, (client_value, client_lower_bound, client_upper_bound))
num_summands = intrinsics.federated_sum(client_one)
secagg_value_type = value.type_signature.member
assert secagg_value_type.is_tensor() or secagg_value_type.is_struct()
if secagg_value_type.is_tensor():
bitwidths = 32
else:
bitwidths = structure.map_structure(lambda t: 32, secagg_value_type)
value = intrinsics.federated_secure_sum(value, bitwidth=bitwidths)
value = intrinsics.federated_map(
server_shift, (value, lower_bound, upper_bound, num_summands))
return value
def test_raises_type_error_with_different_structures(self):
value = intrinsics.federated_value([1, [1, 1]], placement_literals.CLIENTS)
bitwidth = [8, 4, 2]
with self.assertRaises(TypeError):
intrinsics.federated_secure_sum(value, bitwidth)
def test_raises_type_error_with_value_float(self):
value = intrinsics.federated_value(1.0, placements.CLIENTS)
bitwidth = intrinsics.federated_value(1, placements.SERVER)
with self.assertRaises(TypeError):
intrinsics.federated_secure_sum(value, bitwidth)
def secure_aggregation():
data_at_clients = intrinsics.federated_value(1, placements.CLIENTS)
bitwidth = 1
return intrinsics.federated_secure_sum(data_at_clients, bitwidth)
