The ratio of the (effective) noise stddev to the clip.
expected_total_weight
The expected total weight of all clients, used as the
denominator for the average computation.
adaptive_clip_learning_rate
Learning rate for quantile-based adaptive
clipping. If 0, fixed clipping is used. If per-vector clipping is enabled,
(but not geometric_clip_update) the learning rate of each vector is
proportional to that vector's initial clip.
target_unclipped_quantile
Target unclipped quantile for adaptive clipping.
clipped_count_budget_allocation
The fraction of privacy budget to use for
estimating clipped counts.
expected_clients_per_round
The expected number of clients for estimating
clipped fractions.
per_vector_clipping
If True, clip each weight tensor independently.
Otherwise, global clipping is used. The clipping norm for each vector (or
the initial clipping norm, in the case of adaptive clipping) is
proportional to the sqrt of the vector dimensionality such that the root
sum squared of the individual clips equals clip.
geometric_clip_update
If True, use geometric updating of the clip.
model
A tff.learning.Model to determine the structure of model weights.
Required only if per_vector_clipping is True.
Returns
A DPQuery suitable for use in a call to build_dp_aggregate and
build_dp_aggregate_process to perform Federated Averaging with
differential privacy.