tff.utils.build_dp_query

Makes a DPQuery to estimate vector averages with differential privacy.

tff.utils.build_dp_query(
    clip, noise_multiplier, expected_total_weight, adaptive_clip_learning_rate=0,
    target_unclipped_quantile=None, clipped_count_budget_allocation=None,
    expected_clients_per_round=None, geometric_clip_update=True
)

Supports many of the types of query available in tensorflow_privacy, including nested ("per-vector") queries as described in https://arxiv.org/pdf/1812.06210.pdf, and quantile-based adaptive clipping as described in https://arxiv.org/abs/1905.03871

Args
`clip`	The query's L2 norm bound, or the initial clip if adaptive clipping is used.
`noise_multiplier`	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.
`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.
`geometric_clip_update`	If True, use geometric updating of the clip.

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.

tff.utils.build_dp_query

Args

Returns