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.
Args
model_fn
A no-arg function that returns a
tff.learning.models.VariableModel, or an instance of a
tff.learning.models.FunctionalModel. When passing a callable, the
callable 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.
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.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-09-20 UTC."],[],[]]