DatasetSplitFn for Federated Reconstruction training/evaluation.
recon_epochs: int = 1,
recon_steps_max: Optional[int] = None,
post_recon_epochs: int = 1,
post_recon_steps_max: Optional[int] = None,
split_dataset: bool = False
DatasetSplitFn parameterizes training and evaluation computations
and enables reconstruction for multiple local epochs, multiple epochs of
post-reconstruction training, limiting the number of steps for both stages,
and splitting client datasets into disjoint halves for each stage.
Note that the returned function is used during both training and evaluation:
during training, "post-reconstruction" refers to training of global variables,
and during evaluation, it refers to calculation of metrics using reconstructed
local variables and fixed global variables.
The integer number of iterations over the dataset to make
If not None, the integer maximum number of steps (batches)
to iterate through during reconstruction. This maximum number of steps is
across all reconstruction iterations, i.e. it is applied after
recon_epochs. If None, this has no effect.
The integer constant number of iterations to make over
client data after reconstruction.
If not None, the integer maximum number of steps
(batches) to iterate through after reconstruction. This maximum number of
steps is across all post-reconstruction iterations, i.e. it is applied
post_recon_epochs. If None, this has no effect.
If True, splits
client_dataset in half for each user, using
even-indexed entries in reconstruction and odd-indexed entries after
reconstruction. If False,
client_dataset is used for both reconstruction
and post-reconstruction, with the above arguments applied. If True,
splitting requires that mupltiple iterations through the dataset yield the
same ordering. For example if
client_dataset.shuffle(reshuffle_each_iteration=True) has been called,
then the split datasets may have overlap. If True, note that the dataset
should have more than one batch for reasonable results, since the
splitting does not occur within batches.