def test_unweighted_aggregator_raises(self):
bad_aggregator = sum_factory.SumFactory().create(FLOAT_TYPE)
with self.assertRaisesRegex(TypeError, 'weighted'):
composers.compose_learning_process(test_init_model_weights_fn,
test_distributor(),
test_client_work(),
bad_aggregator,
test_finalizer())
def test_not_finalizer_type_raises(self):
finalizer = test_finalizer()
bad_finalizer = measured_process.MeasuredProcess(
finalizer.initialize, finalizer.next)
with self.assertRaisesRegex(TypeError, 'FinalizerProcess'):
composers.compose_learning_process(test_init_model_weights_fn,
test_distributor(),
test_client_work(),
test_aggregator(),
bad_finalizer)
def test_not_tff_computation_init_raises(self):
def init_model_weights_fn():
return model_utils.ModelWeights(trainable=tf.constant(1.0),
non_trainable=())
with self.assertRaisesRegex(TypeError, 'Computation'):
composers.compose_learning_process(init_model_weights_fn,
test_distributor(),
test_client_work(),
test_aggregator(),
test_finalizer())
def test_federated_init_raises(self):
@federated_computation.federated_computation()
def init_model_weights_fn():
return intrinsics.federated_eval(test_init_model_weights_fn,
placements.SERVER)
with self.assertRaisesRegex(TypeError, 'unplaced'):
composers.compose_learning_process(init_model_weights_fn,
test_distributor(),
test_client_work(),
test_aggregator(),
test_finalizer())
def test_one_arg_computation_init_raises(self):
@tensorflow_computation.tf_computation(
computation_types.TensorType(tf.float32))
def init_model_weights_fn(x):
return model_utils.ModelWeights(trainable=x, non_trainable=())
with self.assertRaisesRegex(TypeError, 'Computation'):
composers.compose_learning_process(init_model_weights_fn,
test_distributor(),
test_client_work(),
test_aggregator(),
test_finalizer())
def test_learning_process_composes(self):
process = composers.compose_learning_process(
test_init_model_weights_fn, test_distributor(), test_client_work(),
test_aggregator(), test_finalizer())
self.assertIsInstance(process, learning_process.LearningProcess)
self.assertEqual(
process.initialize.type_signature.result.member.python_container,
composers.LearningAlgorithmState)
self.assertEqual(
process.initialize.type_signature.result.member.
global_model_weights, MODEL_WEIGHTS_TYPE)
# Reported metrics have the expected fields.
metrics_type = process.next.type_signature.result.metrics.member
self.assertTrue(structure.has_field(metrics_type, 'distributor'))
self.assertTrue(structure.has_field(metrics_type, 'client_work'))
self.assertTrue(structure.has_field(metrics_type, 'aggregator'))
self.assertTrue(structure.has_field(metrics_type, 'finalizer'))
self.assertLen(metrics_type, 4)
def build_weighted_mime_lite(
model_fn: Callable[[], model_lib.Model],
base_optimizer: optimizer_base.Optimizer,
server_optimizer: optimizer_base.Optimizer = sgdm.build_sgdm(1.0),
client_weighting: Optional[
client_weight_lib.
ClientWeighting] = client_weight_lib.ClientWeighting.NUM_EXAMPLES,
model_distributor: Optional[distributors.DistributionProcess] = None,
model_aggregator: Optional[factory.WeightedAggregationFactory] = None,
full_gradient_aggregator: Optional[
factory.WeightedAggregationFactory] = None,
metrics_aggregator: Optional[Callable[[
model_lib.MetricFinalizersType, computation_types.StructWithPythonType
], computation_base.Computation]] = None,
use_experimental_simulation_loop: bool = False
) -> learning_process.LearningProcess:
"""Builds a learning process that performs Mime Lite.
This function creates a `tff.learning.templates.LearningProcess` that performs
Mime Lite algorithm on client models. The iterative process has the following
methods inherited from `tff.learning.templates.LearningProcess`:
* `initialize`: A `tff.Computation` with the functional type signature
`( -> S@SERVER)`, where `S` is a
`tff.learning.templates.LearningAlgorithmState` representing the initial
state of the server.
* `next`: A `tff.Computation` with the functional type signature
`(<S@SERVER, {B*}@CLIENTS> -> <L@SERVER>)` where `S` is a
`tff.learning.templates.LearningAlgorithmState` whose type matches the
output of `initialize` and `{B*}@CLIENTS` represents the client datasets.
The output `L` contains the updated server state, as well as aggregated
metrics at the server, including client training metrics and any other
metrics from distribution and aggregation processes.
* `get_model_weights`: A `tff.Computation` with type signature `(S -> M)`,
where `S` is a `tff.learning.templates.LearningAlgorithmState` whose type
matches the output of `initialize` and `next`, and `M` represents the type
of the model weights used during training.
* `set_model_weights`: A `tff.Computation` with type signature
`(<S, M> -> S)`, where `S` is a
`tff.learning.templates.LearningAlgorithmState` whose type matches the
output of `initialize` and `M` represents the type of the model weights
used during training.
Each time the `next` method is called, the server model is communicated to
each client using the provided `model_distributor`. For each client, local
training is performed using `optimizer`, where its state is communicated by
the server, and kept intact during local training. The state is updated only
at the server based on the full gradient evaluated by the clients based on the
current server model state. The client full gradients are aggregated by
weighted `full_gradient_aggregator`. Each client computes the difference
between the client model after training and its initial model. These model
deltas are then aggregated by weighted `model_aggregator`. Both of the
aggregations are weighted, according to `client_weighting`. The aggregate
model delta is added to the existing server model state.
The Mime Lite algorithm is based on the paper
"Breaking the centralized barrier for cross-device federated learning."
Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank
Reddi, Sebastian U. Stich, and Ananda Theertha Suresh.
Advances in Neural Information Processing Systems 34 (2021).
https://proceedings.neurips.cc/paper/2021/file/f0e6be4ce76ccfa73c5a540d992d0756-Paper.pdf
Note that Keras optimizers are not supported. This is due to the Mime Lite
algorithm applying the optimizer without changing it state at clients
(optimizer's `tf.Variable`s in the case of Keras), which is not possible with
Keras optimizers without reaching into private implementation details and
incurring additional computation and memory cost at clients.
Args:
model_fn: A no-arg function that returns a `tff.learning.Model`. This method
must *not* capture TensorFlow tensors or variables and use them. The model
must be constructed entirely from scratch on each invocation, returning
the same pre-constructed model each call will result in an error.
base_optimizer: A `tff.learning.optimizers.Optimizer` which will be used for
both creating and updating a global optimizer state, as well as
optimization at clients given the global state, which is fixed during the
optimization.
server_optimizer: A `tff.learning.optimizers.Optimizer` which will be used
for applying the aggregate model update to the global model weights.
client_weighting: A member of `tff.learning.ClientWeighting` that specifies
a built-in weighting method. By default, weighting by number of examples
is used.
model_distributor: An optional `DistributionProcess` that distributes the
model weights on the server to the clients. If set to `None`, the
distributor is constructed via `distributors.build_broadcast_process`.
model_aggregator: An optional `tff.aggregators.WeightedAggregationFactory`
used to aggregate client updates on the server. If `None`, this is set to
`tff.aggregators.MeanFactory`.
full_gradient_aggregator: An optional
`tff.aggregators.WeightedAggregationFactory` used to aggregate the full
gradients on client datasets. If `None`, this is set to
`tff.aggregators.MeanFactory`.
metrics_aggregator: A function that takes in the metric finalizers (i.e.,
`tff.learning.Model.metric_finalizers()`) and a
`tff.types.StructWithPythonType` of the unfinalized metrics (i.e., the TFF
type of `tff.learning.Model.report_local_unfinalized_metrics()`), and
returns a `tff.Computation` for aggregating the unfinalized metrics. If
`None`, this is set to `tff.learning.metrics.sum_then_finalize`.
use_experimental_simulation_loop: Controls the reduce loop function for
input dataset. An experimental reduce loop is used for simulation. It is
currently necessary to set this flag to True for performant GPU
simulations.
Returns:
A `tff.learning.templates.LearningProcess`.
"""
py_typecheck.check_callable(model_fn)
py_typecheck.check_type(base_optimizer, optimizer_base.Optimizer)
py_typecheck.check_type(server_optimizer, optimizer_base.Optimizer)
py_typecheck.check_type(client_weighting,
client_weight_lib.ClientWeighting)
@tensorflow_computation.tf_computation()
def initial_model_weights_fn():
return model_utils.ModelWeights.from_model(model_fn())
model_weights_type = initial_model_weights_fn.type_signature.result
if model_distributor is None:
model_distributor = distributors.build_broadcast_process(
model_weights_type)
if model_aggregator is None:
model_aggregator = mean.MeanFactory()
py_typecheck.check_type(model_aggregator,
factory.WeightedAggregationFactory)
model_aggregator = model_aggregator.create(
model_weights_type.trainable, computation_types.TensorType(tf.float32))
if full_gradient_aggregator is None:
full_gradient_aggregator = mean.MeanFactory()
py_typecheck.check_type(full_gradient_aggregator,
factory.WeightedAggregationFactory)
client_work = _build_mime_lite_client_work(
model_fn=model_fn,
optimizer=base_optimizer,
client_weighting=client_weighting,
full_gradient_aggregator=full_gradient_aggregator,
metrics_aggregator=metrics_aggregator,
use_experimental_simulation_loop=use_experimental_simulation_loop)
finalizer = finalizers.build_apply_optimizer_finalizer(
server_optimizer, model_weights_type)
return composers.compose_learning_process(initial_model_weights_fn,
model_distributor, client_work,
model_aggregator, finalizer)
def build_weighted_fed_avg_with_optimizer_schedule(
model_fn: Callable[[], model_lib.Model],
client_learning_rate_fn: Callable[[int], float],
client_optimizer_fn: Callable[[float], TFFOrKerasOptimizer],
server_optimizer_fn: Union[optimizer_base.Optimizer, Callable[
[],
tf.keras.optimizers.Optimizer]] = fed_avg.DEFAULT_SERVER_OPTIMIZER_FN,
model_distributor: Optional[distributors.DistributionProcess] = None,
model_aggregator: Optional[factory.WeightedAggregationFactory] = None,
metrics_aggregator: Optional[Callable[[
model_lib.MetricFinalizersType, computation_types.StructWithPythonType
], computation_base.Computation]] = None,
use_experimental_simulation_loop: bool = False
) -> learning_process.LearningProcess:
"""Builds a learning process for FedAvg with client optimizer scheduling.
This function creates a `LearningProcess` that performs federated averaging on
client models. The iterative process has the following methods inherited from
`LearningProcess`:
* `initialize`: A `tff.Computation` with the functional type signature
`( -> S@SERVER)`, where `S` is a `LearningAlgorithmState` representing the
initial state of the server.
* `next`: A `tff.Computation` with the functional type signature
`(<S@SERVER, {B*}@CLIENTS> -> <L@SERVER>)` where `S` is a
`tff.learning.templates.LearningAlgorithmState` whose type matches the
output of `initialize` and `{B*}@CLIENTS` represents the client datasets.
The output `L` contains the updated server state, as well as aggregated
metrics at the server, including client training metrics and any other
metrics from distribution and aggregation processes.
* `get_model_weights`: A `tff.Computation` with type signature `(S -> M)`,
where `S` is a `tff.learning.templates.LearningAlgorithmState` whose type
matches the output of `initialize` and `next`, and `M` represents the type
of the model weights used during training.
* `set_model_weights`: A `tff.Computation` with type signature
`(<S, M> -> S)`, where `S` is a
`tff.learning.templates.LearningAlgorithmState` whose type matches the
output of `initialize` and `M` represents the type of the model weights
used during training.
Each time the `next` method is called, the server model is broadcast to each
client using a broadcast function. For each client, local training is
performed using `client_optimizer_fn`. Each client computes the difference
between the client model after training and the initial broadcast model.
These model deltas are then aggregated at the server using a weighted
aggregation function. Clients weighted by the number of examples they see
thoughout local training. The aggregate model delta is applied at the server
using a server optimizer.
The primary purpose of this implementation of FedAvg is that it allows for the
client optimizer to be scheduled across rounds. The process keeps track of how
many iterations of `.next` have occurred (starting at `0`), and for each such
`round_num`, the clients will use `client_optimizer_fn(round_num)` to perform
local optimization. This allows learning rate scheduling (eg. starting with
a large learning rate and decaying it over time) as well as a small learning
rate (eg. switching optimizers as learning progresses).
Note: the default server optimizer function is `tf.keras.optimizers.SGD`
with a learning rate of 1.0, which corresponds to adding the model delta to
the current server model. This recovers the original FedAvg algorithm in
[McMahan et al., 2017](https://arxiv.org/abs/1602.05629). More
sophisticated federated averaging procedures may use different learning rates
or server optimizers.
Args:
model_fn: A no-arg function that returns a `tff.learning.Model`. This method
must *not* capture TensorFlow tensors or variables and use them. The model
must be constructed entirely from scratch on each invocation, returning
the same pre-constructed model each call will result in an error.
client_learning_rate_fn: A callable accepting an integer round number and
returning a float to be used as a learning rate for the optimizer. The
client work will call `optimizer_fn(learning_rate_fn(round_num))` where
`round_num` is the integer round number. Note that the round numbers
supplied will start at `0` and increment by one each time `.next` is
called on the resulting process. Also note that this function must be
serializable by TFF.
client_optimizer_fn: A callable accepting a float learning rate, and
returning a `tff.learning.optimizers.Optimizer` or a `tf.keras.Optimizer`.
server_optimizer_fn: A `tff.learning.optimizers.Optimizer`, or a no-arg
callable that returns a `tf.keras.Optimizer`. By default, this uses
`tf.keras.optimizers.SGD` with a learning rate of 1.0.
model_distributor: An optional `DistributionProcess` that distributes the
model weights on the server to the clients. If set to `None`, the
distributor is constructed via `distributors.build_broadcast_process`.
model_aggregator: An optional `tff.aggregators.WeightedAggregationFactory`
used to aggregate client updates on the server. If `None`, this is set to
`tff.aggregators.MeanFactory`.
metrics_aggregator: A function that takes in the metric finalizers (i.e.,
`tff.learning.Model.metric_finalizers()`) and a
`tff.types.StructWithPythonType` of the unfinalized metrics (i.e., the TFF
type of `tff.learning.Model.report_local_unfinalized_metrics()`), and
returns a `tff.Computation` for aggregating the unfinalized metrics. If
`None`, this is set to `tff.learning.metrics.sum_then_finalize`.
use_experimental_simulation_loop: Controls the reduce loop function for
input dataset. An experimental reduce loop is used for simulation. It is
currently necessary to set this flag to True for performant GPU
simulations.
Returns:
A `LearningProcess`.
"""
py_typecheck.check_callable(model_fn)
@tensorflow_computation.tf_computation()
def initial_model_weights_fn():
return model_utils.ModelWeights.from_model(model_fn())
model_weights_type = initial_model_weights_fn.type_signature.result
if model_distributor is None:
model_distributor = distributors.build_broadcast_process(model_weights_type)
if model_aggregator is None:
model_aggregator = mean.MeanFactory()
py_typecheck.check_type(model_aggregator, factory.WeightedAggregationFactory)
aggregator = model_aggregator.create(model_weights_type.trainable,
computation_types.TensorType(tf.float32))
process_signature = aggregator.next.type_signature
input_client_value_type = process_signature.parameter[1]
result_server_value_type = process_signature.result[1]
if input_client_value_type.member != result_server_value_type.member:
raise TypeError('`model_update_aggregation_factory` does not produce a '
'compatible `AggregationProcess`. The processes must '
'retain the type structure of the inputs on the '
f'server, but got {input_client_value_type.member} != '
f'{result_server_value_type.member}.')
if metrics_aggregator is None:
metrics_aggregator = metric_aggregator.sum_then_finalize
client_work = build_scheduled_client_work(model_fn, client_learning_rate_fn,
client_optimizer_fn,
metrics_aggregator,
use_experimental_simulation_loop)
finalizer = finalizers.build_apply_optimizer_finalizer(
server_optimizer_fn, model_weights_type)
return composers.compose_learning_process(initial_model_weights_fn,
model_distributor, client_work,
aggregator, finalizer)
def build_fed_kmeans(
num_clusters: int,
data_shape: Tuple[int, ...],
random_seed: Optional[Tuple[int, int]] = None,
distributor: Optional[distributors.DistributionProcess] = None,
sum_aggregator: Optional[factory.UnweightedAggregationFactory] = None,
) -> learning_process.LearningProcess:
"""Builds a learning process for federated k-means clustering.
This function creates a `tff.learning.templates.LearningProcess` that performs
federated k-means clustering. Specifically, this performs mini-batch k-means
clustering. Note that mini-batch k-means only processes a mini-batch of the
data at each round, and updates clusters in a weighted manner based on how
many points in the mini-batch were assigned to each cluster. In the federated
version, clients do the assignment of each of their point locally, and the
server updates the clusters. Conceptually, the "mini-batch" being used is the
union of all client datasets involved in a given round.
The learning process has the following methods inherited from
`tff.learning.templates.LearningProcess`:
* `initialize`: A `tff.Computation` with the functional type signature
`( -> S@SERVER)`, where `S` is a `LearningAlgorithmState` representing the
initial state of the server.
* `next`: A `tff.Computation` with the functional type signature
`(<S@SERVER, {B*}@CLIENTS> -> <L@SERVER>)` where `S` is a
`LearningAlgorithmState` whose type matches the output of `initialize`
and `{B*}@CLIENTS` represents the client datasets. The output `L` is a
`tff.learning.templates.LearningProcessOutput` containing the state `S`
and metrics computed during training.
* `get_model_weights`: A `tff.Computation` with type signature `(S -> W)`,
where `W` represents the current k-means centroids.
* `set_model_weights`: A `tff.Computation` with type signature
`(<S, M> -> S)`, where `S` is a
`tff.learning.templates.LearningAlgorithmState` whose type matches the
output of `initialize` and `M` a new set of k-means centroids.
Here, `S` is a `tff.learning.templates.LearningAlgorithmState`. The centroids
`W` is a tensor representing the current centroids, and is of shape
`(num_clusters,) + data_shape`. The datasets `{B*}` must have elements of
shape `data_shape`, and not employ batching.
The centroids are updated at each round by assigning all clients' points to
the nearest centroid, and then summing these points according to these
centroids. The centroids are then updated at the server based on these points.
To do so, we keep track of how many points have been assigned to each centroid
overall, as an integer tensor of shape `(num_clusters,)`. This information can
be found in `state.finalizer`. Note that we begin with a "pseudo-count" of 1,
in order to ensure that the centroids do not collapse to zero.
Args:
num_clusters: The number of clusters to use.
data_shape: A tuple of integers specifying the shape of each data point.
Note that this data shape should be unbatched, as this algorithm does not
currently support batched data points.
random_seed: A tuple of two integers used to seed the initialization phase.
distributor: An optional `tff.learning.tekmplates.DistributionProcess` that
broadcasts the centroids on the server to the clients. If set to `None`,
the distributor is constructed via
`tff.learning.templates.build_broadcast_process`.
sum_aggregator: An optional `tff.aggregators.UnweightedAggregationFactory`
used to sum updates across clients. If `None`, we use
`tff.aggregators.SumFactory`.
Returns:
A `LearningProcess`.
"""
centroids_shape = (num_clusters, ) + data_shape
if not random_seed:
random_seed = (tf.cast(tf.timestamp() * _MILLIS_PER_SECOND,
tf.int64).numpy(), 0)
@tensorflow_computation.tf_computation
def initialize_centers():
return tf.random.stateless_normal(centroids_shape,
random_seed,
dtype=_POINT_DTYPE)
centroids_type = computation_types.TensorType(_POINT_DTYPE,
centroids_shape)
weights_type = computation_types.TensorType(_WEIGHT_DTYPE,
shape=(num_clusters, ))
point_type = computation_types.TensorType(_POINT_DTYPE, shape=data_shape)
data_type = computation_types.SequenceType(point_type)
if distributor is None:
distributor = distributors.build_broadcast_process(centroids_type)
client_work = _build_kmeans_client_work(centroids_type, data_type)
if sum_aggregator is None:
sum_aggregator = sum_factory.SumFactory()
# We wrap the sum factory as a weighted aggregator for compatibility with
# the learning process composer.
weighted_aggregator = factory_utils.as_weighted_aggregator(sum_aggregator)
value_type = computation_types.to_type((centroids_type, weights_type))
aggregator = weighted_aggregator.create(value_type,
computation_types.to_type(()))
finalizer = _build_kmeans_finalizer(centroids_type, num_clusters)
return composers.compose_learning_process(initialize_centers, distributor,
client_work, aggregator,
finalizer)
def build_fed_sgd(
model_fn: Callable[[], model_lib.Model],
server_optimizer_fn: Union[optimizer_base.Optimizer, Callable[
[], tf.keras.optimizers.Optimizer]] = DEFAULT_SERVER_OPTIMIZER_FN,
model_distributor: Optional[distributors.DistributionProcess] = None,
model_aggregator: Optional[factory.WeightedAggregationFactory] = None,
metrics_aggregator: Optional[Callable[[
model_lib.MetricFinalizersType, computation_types.StructWithPythonType
], computation_base.Computation]] = None,
use_experimental_simulation_loop: bool = False,
) -> learning_process.LearningProcess:
"""Builds a learning process that performs federated SGD.
This function creates a `tff.learning.templates.LearningProcess` that performs
federated SGD on client models. The learning process has the following methods
inherited from `tff.learning.templates.LearningProcess`:
* `initialize`: A `tff.Computation` with type signature `( -> S@SERVER)`,
where `S` is a `tff.learning.templates.LearningAlgorithmState`
representing the initial state of the server.
* `next`: A `tff.Computation` with type signature
`(<S@SERVER, {B*}@CLIENTS> -> <S@SERVER, T@SERVER>)` where `S` is a
`LearningAlgorithmState` whose type matches that of the output
of `initialize`, and `{B*}@CLIENTS` represents the client datasets, where
`B` is the type of a single batch. This computation returns a
`LearningAlgorithmState` representing the updated server state and the
metrics during client training and any other metrics from broadcast and
aggregation processes.
* `get_model_weights`: A `tff.Computation` with type signature `(S -> M)`,
where `S` is a `tff.learning.templates.LearningAlgorithmState` whose type
matches the output of `initialize` and `next`, and `M` represents the type
of the model weights used during training.
* `set_model_weights`: A `tff.Computation` with type signature
`(<S, M> -> S)`, where `S` is a
`tff.learning.templates.LearningAlgorithmState` whose type matches the
output of `initialize` and `M` represents the type of the model weights
used during training.
Each time `next` is called, the server model is broadcast to each client using
a distributor. Each client sums the gradients for each batch in its local
dataset (without updating its model) to calculate, and averages the gradients
based on their number of examples. These average gradients are then aggregated
at the server, and are applied at the server using a
`tf.keras.optimizers.Optimizer`.
This implements the original FedSGD algorithm in [McMahan et al.,
2017](https://arxiv.org/abs/1602.05629).
Args:
model_fn: A no-arg function that returns a `tff.learning.Model`. This method
must *not* capture TensorFlow tensors or variables and use them. The model
must be constructed entirely from scratch on each invocation, returning
the same pre-constructed model each call will result in an error.
server_optimizer_fn: A `tff.learning.optimizers.Optimizer`, or a no-arg
callable that returns a `tf.keras.Optimizer`. The optimizer is used to
apply client updates to the server model.
model_distributor: An optional `DistributionProcess` that distributes the
model weights on the server to the clients. If set to `None`, the
distributor is constructed via `distributors.build_broadcast_process`.
model_aggregator: An optional `tff.aggregators.WeightedAggregationFactory`
used to aggregate client updates on the server. If `None`, this is set to
`tff.aggregators.MeanFactory`.
metrics_aggregator: A function that takes in the metric finalizers (i.e.,
`tff.learning.Model.metric_finalizers()`) and a
`tff.types.StructWithPythonType` of the unfinalized metrics (i.e., the TFF
type of `tff.learning.Model.report_local_unfinalized_metrics()`), and
returns a `tff.Computation` for aggregating the unfinalized metrics.
use_experimental_simulation_loop: Controls the reduce loop function for
input dataset. An experimental reduce loop is used for simulation.
Returns:
A `tff.learning.templates.LearningProcess`.
"""
py_typecheck.check_callable(model_fn)
@tensorflow_computation.tf_computation()
def initial_model_weights_fn():
return model_utils.ModelWeights.from_model(model_fn())
model_weights_type = initial_model_weights_fn.type_signature.result
if model_distributor is None:
model_distributor = distributors.build_broadcast_process(
model_weights_type)
if model_aggregator is None:
model_aggregator = mean.MeanFactory()
aggregator = model_aggregator.create(
model_weights_type.trainable, computation_types.TensorType(tf.float32))
if metrics_aggregator is None:
metrics_aggregator = metric_aggregator.sum_then_finalize
client_work = _build_fed_sgd_client_work(
model_fn,
metrics_aggregator,
use_experimental_simulation_loop=use_experimental_simulation_loop)
finalizer = finalizers.build_apply_optimizer_finalizer(
server_optimizer_fn, model_weights_type)
return composers.compose_learning_process(initial_model_weights_fn,
model_distributor, client_work,
aggregator, finalizer)
def build_weighted_fed_prox(
model_fn: Callable[[], model_lib.Model],
proximal_strength: float,
client_optimizer_fn: Union[optimizer_base.Optimizer,
Callable[[], tf.keras.optimizers.Optimizer]],
server_optimizer_fn: Union[optimizer_base.Optimizer, Callable[
[], tf.keras.optimizers.Optimizer]] = DEFAULT_SERVER_OPTIMIZER_FN,
client_weighting: Optional[
client_weight_lib.ClientWeighting] = client_weight_lib.ClientWeighting.
NUM_EXAMPLES,
model_distributor: Optional[distributors.DistributionProcess] = None,
model_aggregator: Optional[factory.WeightedAggregationFactory] = None,
metrics_aggregator: Optional[Callable[[
model_lib.MetricFinalizersType, computation_types.StructWithPythonType
], computation_base.Computation]] = None,
use_experimental_simulation_loop: bool = False
) -> learning_process.LearningProcess:
"""Builds a learning process that performs the FedProx algorithm.
This function creates a `tff.learning.templates.LearningProcess` that performs
example-weighted FedProx on client models. This algorithm behaves the same as
federated averaging, except that it uses a proximal regularization term that
encourages clients to not drift too far from the server model.
The iterative process has the following methods inherited from
`tff.learning.templates.LearningProcess`:
* `initialize`: A `tff.Computation` with the functional type signature
`( -> S@SERVER)`, where `S` is a
`tff.learning.templates.LearningAlgorithmState` representing the initial
state of the server.
* `next`: A `tff.Computation` with the functional type signature
`(<S@SERVER, {B*}@CLIENTS> -> <L@SERVER>)` where `S` is a
`tff.learning.templates.LearningAlgorithmState` whose type matches the
output of `initialize`and `{B*}@CLIENTS` represents the client datasets.
The output `L` contains the updated server state, as well as aggregated
metrics at the server, including client training metrics and any other
metrics from distribution and aggregation processes.
* `get_model_weights`: A `tff.Computation` with type signature `(S -> M)`,
where `S` is a `tff.learning.templates.LearningAlgorithmState` whose type
matches the output of `initialize` and `next`, and `M` represents the type
of the model weights used during training.
* `set_model_weights`: A `tff.Computation` with type signature
`(<S, M> -> S)`, where `S` is a
`tff.learning.templates.LearningAlgorithmState` whose type matches the
output of `initialize` and `M` represents the type of the model weights
used during training.
Each time the `next` method is called, the server model is communicated to
each client using the provided `model_distributor`. For each client, local
training is performed using `client_optimizer_fn`. Each client computes the
difference between the client model after training and the initial model.
These model deltas are then aggregated at the server using a weighted
aggregation function, according to `client_weighting`. The aggregate model
delta is applied at the server using a server optimizer, as in the FedOpt
framework proposed in [Reddi et al., 2021](https://arxiv.org/abs/2003.00295).
Note: The default server optimizer function is `tf.keras.optimizers.SGD`
with a learning rate of 1.0, which corresponds to adding the model delta to
the current server model. This recovers the original FedProx algorithm in
[Li et al., 2020](https://arxiv.org/abs/1812.06127). More
sophisticated federated averaging procedures may use different learning rates
or server optimizers.
Args:
model_fn: A no-arg function that returns a `tff.learning.Model`. This method
must *not* capture TensorFlow tensors or variables and use them. The model
must be constructed entirely from scratch on each invocation, returning
the same pre-constructed model each call will result in an error.
proximal_strength: A nonnegative float representing the parameter of
FedProx's regularization term. When set to `0`, the algorithm reduces to
FedAvg. Higher values prevent clients from moving too far from the server
model during local training.
client_optimizer_fn: A `tff.learning.optimizers.Optimizer`, or a no-arg
callable that returns a `tf.keras.Optimizer`.
server_optimizer_fn: A `tff.learning.optimizers.Optimizer`, or a no-arg
callable that returns a `tf.keras.Optimizer`. By default, this uses
`tf.keras.optimizers.SGD` with a learning rate of 1.0.
client_weighting: A member of `tff.learning.ClientWeighting` that specifies
a built-in weighting method. By default, weighting by number of examples
is used.
model_distributor: An optional `DistributionProcess` that broadcasts the
model weights on the server to the clients. If set to `None`, the
distributor is constructed via `distributors.build_broadcast_process`.
model_aggregator: An optional `tff.aggregators.WeightedAggregationFactory`
used to aggregate client updates on the server. If `None`, this is set to
`tff.aggregators.MeanFactory`.
metrics_aggregator: A function that takes in the metric finalizers (i.e.,
`tff.learning.Model.metric_finalizers()`) and a
`tff.types.StructWithPythonType` of the unfinalized metrics (i.e., the TFF
type of `tff.learning.Model.report_local_unfinalized_metrics()`), and
returns a `tff.Computation` for aggregating the unfinalized metrics. If
`None`, this is set to `tff.learning.metrics.sum_then_finalize`.
use_experimental_simulation_loop: Controls the reduce loop function for
input dataset. An experimental reduce loop is used for simulation. It is
currently necessary to set this flag to True for performant GPU
simulations.
Returns:
A `tff.learning.templates.LearningProcess`.
Raises:
ValueError: If `proximal_parameter` is not a nonnegative float.
"""
if not isinstance(proximal_strength, float) or proximal_strength < 0.0:
raise ValueError(
'proximal_strength must be a nonnegative float, found {}'.format(
proximal_strength))
py_typecheck.check_callable(model_fn)
@tensorflow_computation.tf_computation()
def initial_model_weights_fn():
return model_utils.ModelWeights.from_model(model_fn())
model_weights_type = initial_model_weights_fn.type_signature.result
if model_distributor is None:
model_distributor = distributors.build_broadcast_process(
model_weights_type)
if model_aggregator is None:
model_aggregator = mean.MeanFactory()
py_typecheck.check_type(model_aggregator,
factory.WeightedAggregationFactory)
aggregator = model_aggregator.create(
model_weights_type.trainable, computation_types.TensorType(tf.float32))
process_signature = aggregator.next.type_signature
input_client_value_type = process_signature.parameter[1]
result_server_value_type = process_signature.result[1]
if input_client_value_type.member != result_server_value_type.member:
raise TypeError(
'`model_update_aggregation_factory` does not produce a '
'compatible `AggregationProcess`. The processes must '
'retain the type structure of the inputs on the '
f'server, but got {input_client_value_type.member} != '
f'{result_server_value_type.member}.')
if metrics_aggregator is None:
metrics_aggregator = metric_aggregator.sum_then_finalize
client_work = model_delta_client_work.build_model_delta_client_work(
model_fn=model_fn,
optimizer=client_optimizer_fn,
client_weighting=client_weighting,
delta_l2_regularizer=proximal_strength,
metrics_aggregator=metrics_aggregator,
use_experimental_simulation_loop=use_experimental_simulation_loop)
finalizer = finalizers.build_apply_optimizer_finalizer(
server_optimizer_fn, model_weights_type)
return composers.compose_learning_process(initial_model_weights_fn,
model_distributor, client_work,
aggregator, finalizer)
def build_weighted_fed_avg(
model_fn: Callable[[], model_lib.Model],
client_optimizer_fn: Union[optimizer_base.Optimizer,
Callable[[], tf.keras.optimizers.Optimizer]],
server_optimizer_fn: Union[optimizer_base.Optimizer, Callable[
[], tf.keras.optimizers.Optimizer]] = DEFAULT_SERVER_OPTIMIZER_FN,
*,
client_weighting: Optional[
client_weight_lib.ClientWeighting] = client_weight_lib.ClientWeighting.
NUM_EXAMPLES,
model_distributor: Optional[distributors.DistributionProcess] = None,
model_aggregator: Optional[factory.WeightedAggregationFactory] = None,
metrics_aggregator: Optional[Callable[[
model_lib.MetricFinalizersType, computation_types.StructWithPythonType
], computation_base.Computation]] = None,
use_experimental_simulation_loop: bool = False,
model: Optional[functional.FunctionalModel] = None,
) -> learning_process.LearningProcess:
"""Builds a learning process that performs federated averaging.
This function creates a `tff.learning.templates.LearningProcess` that performs
federated averaging on client models. The iterative process has the following
methods inherited from `tff.learning.templates.LearningProcess`:
* `initialize`: A `tff.Computation` with the functional type signature
`( -> S@SERVER)`, where `S` is a
`tff.learning.templates.LearningAlgorithmState` representing the initial
state of the server.
* `next`: A `tff.Computation` with the functional type signature
`(<S@SERVER, {B*}@CLIENTS> -> <L@SERVER>)` where `S` is a
`tff.learning.templates.LearningAlgorithmState` whose type matches the
output of `initialize` and `{B*}@CLIENTS` represents the client datasets.
The output `L` contains the updated server state, as well as aggregated
metrics at the server, including client training metrics and any other
metrics from distribution and aggregation processes.
* `get_model_weights`: A `tff.Computation` with type signature `(S -> M)`,
where `S` is a `tff.learning.templates.LearningAlgorithmState` whose type
matches the output of `initialize` and `next`, and `M` represents the type
of the model weights used during training.
* `set_model_weights`: A `tff.Computation` with type signature
`(<S, M> -> S)`, where `S` is a
`tff.learning.templates.LearningAlgorithmState` whose type matches the
output of `initialize` and `M` represents the type of the model weights
used during training.
Each time the `next` method is called, the server model is communicated to
each client using the provided `model_distributor`. For each client, local
training is performed using `client_optimizer_fn`. Each client computes the
difference between the client model after training and its initial model.
These model deltas are then aggregated at the server using a weighted
aggregation function, according to `client_weighting`. The aggregate model
delta is applied at the server using a server optimizer.
Note: the default server optimizer function is `tf.keras.optimizers.SGD`
with a learning rate of 1.0, which corresponds to adding the model delta to
the current server model. This recovers the original FedAvg algorithm in
[McMahan et al., 2017](https://arxiv.org/abs/1602.05629). More
sophisticated federated averaging procedures may use different learning rates
or server optimizers (this generalized FedAvg algorithm is described in
[Reddi et al., 2021](https://arxiv.org/abs/2003.00295)).
Args:
model_fn: A no-arg function that returns a `tff.learning.Model`. This method
must *not* capture TensorFlow tensors or variables and use them. The model
must be constructed entirely from scratch on each invocation, returning
the same pre-constructed model each call will result in an error. Cannot
be used with `model` argument.
client_optimizer_fn: A `tff.learning.optimizers.Optimizer`, or a no-arg
callable that returns a `tf.keras.Optimizer`.
server_optimizer_fn: A `tff.learning.optimizers.Optimizer`, or a no-arg
callable that returns a `tf.keras.Optimizer`. By default, this uses
`tf.keras.optimizers.SGD` with a learning rate of 1.0.
client_weighting: A member of `tff.learning.ClientWeighting` that specifies
a built-in weighting method. By default, weighting by number of examples
is used.
model_distributor: An optional `DistributionProcess` that distributes the
model weights on the server to the clients. If set to `None`, the
distributor is constructed via
`tff.learning.templates.build_broadcast_process`.
model_aggregator: An optional `tff.aggregators.WeightedAggregationFactory`
used to aggregate client updates on the server. If `None`, this is set to
`tff.aggregators.MeanFactory`.
metrics_aggregator: A function that takes in the metric finalizers (i.e.,
`tff.learning.Model.metric_finalizers()`) and a
`tff.types.StructWithPythonType` of the unfinalized metrics (i.e., the TFF
type of `tff.learning.Model.report_local_unfinalized_metrics()`), and
returns a `tff.Computation` for aggregating the unfinalized metrics. If
`None`, this is set to `tff.learning.metrics.sum_then_finalize`.
use_experimental_simulation_loop: Controls the reduce loop function for
input dataset. An experimental reduce loop is used for simulation. It is
currently necessary to set this flag to True for performant GPU
simulations.
model: A `tff.learning.models.FunctionalModel` to train. Cannot be used with
`model_fn` and must be `None` if `model_fn` is not `None`.
Returns:
A `tff.learning.templates.LearningProcess`.
"""
if model is not None and model_fn is not None:
raise ValueError(
'Must specify only one of `model` and `model_fn`, both '
'were not `None`.')
elif model is None and model_fn is None:
raise ValueError(
'Must specify one of `model` and `model_fn`, both were '
'`None`.')
if model_fn is not None:
py_typecheck.check_callable(model_fn)
else:
py_typecheck.check_type(model, functional.FunctionalModel)
py_typecheck.check_type(client_weighting,
client_weight_lib.ClientWeighting)
if model is not None:
@tensorflow_computation.tf_computation()
def initial_model_weights_fn():
return model_utils.ModelWeights(
tuple(
tf.convert_to_tensor(w) for w in model.initial_weights[0]),
tuple(
tf.convert_to_tensor(w) for w in model.initial_weights[1]))
else:
@tensorflow_computation.tf_computation()
def initial_model_weights_fn():
return model_utils.ModelWeights.from_model(model_fn())
model_weights_type = initial_model_weights_fn.type_signature.result
if model_distributor is None:
model_distributor = distributors.build_broadcast_process(
model_weights_type)
if model_aggregator is None:
model_aggregator = mean.MeanFactory()
py_typecheck.check_type(model_aggregator,
factory.WeightedAggregationFactory)
if model is not None:
trainable_weights_type, _ = model_weights_type
model_update_type = trainable_weights_type
else:
model_update_type = model_weights_type.trainable
aggregator = model_aggregator.create(
model_update_type, computation_types.TensorType(tf.float32))
process_signature = aggregator.next.type_signature
input_client_value_type = process_signature.parameter[1]
result_server_value_type = process_signature.result[1]
if input_client_value_type.member != result_server_value_type.member:
raise TypeError(
'`model_aggregator` does not produce a compatible '
'`AggregationProcess`. The processes must retain the type '
'structure of the inputs on the server, but got '
f'{input_client_value_type.member} != '
f'{result_server_value_type.member}.')
if metrics_aggregator is None:
metrics_aggregator = metric_aggregator.sum_then_finalize
if model is not None:
client_work = model_delta_client_work.build_functional_model_delta_client_work(
model=model,
optimizer=client_optimizer_fn,
client_weighting=client_weighting,
metrics_aggregator=metrics_aggregator)
else:
client_work = model_delta_client_work.build_model_delta_client_work(
model_fn=model_fn,
optimizer=client_optimizer_fn,
client_weighting=client_weighting,
metrics_aggregator=metrics_aggregator,
use_experimental_simulation_loop=use_experimental_simulation_loop)
finalizer = finalizers.build_apply_optimizer_finalizer(
server_optimizer_fn, model_weights_type)
return composers.compose_learning_process(initial_model_weights_fn,
model_distributor, client_work,
aggregator, finalizer)