tff.aggregators.DifferentiallyPrivateFactory

See Stable See Nightly

UnweightedAggregationFactory for tensorflow_privacy DPQueries.

Inherits From: UnweightedAggregationFactory

tff.aggregators.DifferentiallyPrivateFactory(
    query: tfp.DPQuery,
    record_aggregation_factory: Optional[tff.aggregators.UnweightedAggregationFactory] = None
)

Used in the notebooks

Used in the tutorials
Tuning recommended aggregations for learning

The created tff.templates.AggregationProcess aggregates values placed at CLIENTS according to the provided DPQuery, and outputs the result placed at SERVER.

A DPQuery defines preprocessing to perform on each value, and postprocessing to perform on the aggregated, preprocessed values. Provided the preprocessed values ("records") are aggregated in a way that is consistent with the DPQuery, formal (epsilon, delta) privacy guarantees can be derived. This aggregation is controlled by record_aggregation_factory.

A simple summation (using the default tff.aggregators.SumFactory) is usually acceptable. Aggregations that change the records (such as compression or secure aggregation) may be allowed so long as they do not increase the sensitivity of the query. It is the users' responsibility to ensure that the mode of aggregation is consistent with the DPQuery. Note that the DPQuery's built-in aggregation functions (accumulate_preprocessed_record and merge_sample_states) are ignored in favor of the provided aggregator.

To obtain concrete (epsilon, delta) guarantees, one could use the analysis tools provided in tensorflow_privacy by using QueryWithLedger.

Args
query	A tfp.SumAggregationDPQuery to perform private estimation.
record_aggregation_factory	A tff.aggregators.UnweightedAggregationFactory to aggregate values after preprocessing by the query. If None, defaults to tff.aggregators.SumFactory. The provided factory is assumed to implement a sum, and to have the property that it does not increase the sensitivity of the query - typically this means that it should not increase the l2 norm of the records when aggregating.

Raises
TypeError	If query is not an instance of tfp.SumAggregationDPQuery or record_aggregation_factory is not an instance of tff.aggregators.UnweightedAggregationFactory.

Methods

create

View source

create(
    value_type: factory.ValueType
) -> tff.templates.AggregationProcess

Creates a tff.aggregators.AggregationProcess without weights.

The provided value_type is a non-federated tff.Type object, that is, value_type.is_federated() should return False.

The returned tff.aggregators.AggregationProcess will be created for aggregation of values matching value_type placed at tff.CLIENTS. That is, its next method will expect type <S@SERVER, {value_type}@CLIENTS>, where S is the unplaced return type of its initialize method.

Args
value_type	A non-federated tff.Type (value_type.is_federated() returns False).

Returns
A tff.templates.AggregationProcess.

gaussian_adaptive

View source

@classmethod
gaussian_adaptive(
    noise_multiplier: float,
    clients_per_round: float,
    initial_l2_norm_clip: float = 0.1,
    target_unclipped_quantile: float = 0.5,
    learning_rate: float = 0.2,
    clipped_count_stddev: Optional[float] = None
) -> tff.aggregators.UnweightedAggregationFactory

DifferentiallyPrivateFactory with adaptive clipping and Gaussian noise.

Performs adaptive clipping and addition of Gaussian noise for differentially private learning. For details of the DP algorithm see McMahan et. al (2017) https://arxiv.org/abs/1710.06963 The adaptive clipping uses the geometric method described in Thakkar et al. (2019) https://arxiv.org/abs/1905.03871

The adaptive clipping parameters have been chosen to yield a process that starts small and adapts relatively quickly to the median, without using much of the privacy budget. This works well on most problems.

Args
noise_multiplier	A float specifying the noise multiplier for the Gaussian mechanism for model updates. A value of 1.0 or higher may be needed for strong privacy. See above mentioned papers to compute (epsilon, delta) privacy guarantee. Note that this is the effective total noise multiplier, accounting for the privacy loss due to adaptive clipping. The noise actually added to the aggregated values will be slightly higher.
clients_per_round	A float specifying the expected number of clients per round. Must be positive.
initial_l2_norm_clip	The initial value of the adaptive clipping norm.
target_unclipped_quantile	The quantile to which the clipping norm should adapt.
learning_rate	The learning rate for the adaptive clipping process.
clipped_count_stddev	The stddev of the noise added to the clipped counts in the adaptive clipping algorithm. If None, defaults to 0.05 * clients_per_round.

Returns
A DifferentiallyPrivateFactory with adaptive clipping and Gaussian noise.

gaussian_fixed

View source

@classmethod
gaussian_fixed(
    noise_multiplier: float,
    clients_per_round: float,
    clip: float
) -> tff.aggregators.UnweightedAggregationFactory

DifferentiallyPrivateFactory with fixed clipping and Gaussian noise.

Performs fixed clipping and addition of Gaussian noise for differentially private learning. For details of the DP algorithm see McMahan et. al (2017) https://arxiv.org/abs/1710.06963

Args
noise_multiplier	A float specifying the noise multiplier for the Gaussian mechanism for model updates. A value of 1.0 or higher may be needed for strong privacy. See above mentioned paper to compute (epsilon, delta) privacy guarantee.
clients_per_round	A float specifying the expected number of clients per round. Must be positive.
clip	The value of the clipping norm.

Returns
A DifferentiallyPrivateFactory with fixed clipping and Gaussian noise.