tff.aggregators.zeroing_factory
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Creates an aggregation factory to perform zeroing.
tff.aggregators.zeroing_factory(
zeroing_norm: Union[float, tff.templates.EstimationProcess],
inner_agg_factory: _T,
norm_order: float = math.inf,
zeroed_count_sum_factory: Optional[tff.aggregators.UnweightedAggregationFactory] = None
) -> _T
Used in the notebooks
The created tff.templates.AggregationProcess zeroes out any values whose
norm is greater than that determined by the provided zeroing_norm, before
aggregating the values as specified by inner_agg_factory. Note that for
weighted aggregation if some value is zeroed, the weight is unchanged. So for
example if you have a zeroed weighted mean and a lot of zeroing occurs, the
average will tend to be pulled toward zero. This is for consistency between
weighted and unweighted aggregation
The provided zeroing_norm can either be a constant (for fixed norm), or an
instance of tff.templates.EstimationProcess (for adaptive norm). If it is an
estimation process, the value returned by its report method will be used as
the zeroing norm. Its next method needs to accept a scalar float32 at
clients, corresponding to the norm of value being aggregated. The process can
thus adaptively determine the zeroing norm based on the set of aggregated
values. For example if a tff.aggregators.PrivateQuantileEstimationProcess is
used, the zeroing norm will be an estimate of a quantile of the norms of the
values being aggregated.
The value_type provided to the create method must be a structure of
floats, but they do not all need to be the same, e.g. a mix of np.float32
and np.float16 dtypes is allowed.
The created process will report measurements
zeroed_count: The number of aggregands zeroed out.zeroing_norm: The norm used to determine whether to zero out an aggregand.
The returned AggregationFactory takes its weightedness
(UnweightedAggregationFactory vs. WeightedAggregationFactory) from
inner_agg_factory.
Args |
|---|
zeroing_norm
|
Either a float (for fixed norm) or an EstimationProcess (for
adaptive norm) that specifies the norm over which the values should be
zeroed.
|
inner_agg_factory
|
A factory specifying the type of aggregation to be done
after zeroing.
|
norm_order
|
A float for the order of the norm. Must be 1., 2., or infinity.
|
zeroed_count_sum_factory
|
A factory specifying the type of aggregation done
for zeroed_count measurement. If None, tff.aggregators.SumFactory
will be used.
|
Returns |
|---|
|
An aggregation factory to perform zeroing.
|
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Last updated 2024-09-20 UTC.
[null,null,["Last updated 2024-09-20 UTC."],[],[],null,["# tff.aggregators.zeroing_factory\n\n\u003cbr /\u003e\n\n|-------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/federated/blob/v0.87.0 Version 2.0, January 2004 Licensed under the Apache License, Version 2.0 (the) |\n\nCreates an aggregation factory to perform zeroing. \n\n tff.aggregators.zeroing_factory(\n zeroing_norm: Union[float, ../../tff/templates/EstimationProcess],\n inner_agg_factory: _T,\n norm_order: float = math.inf,\n zeroed_count_sum_factory: Optional[../../tff/aggregators/UnweightedAggregationFactory] = None\n ) -\u003e _T\n\n### Used in the notebooks\n\n| Used in the tutorials |\n|---------------------------------------------------------------------------------------------------------------------------------|\n| - [Tuning recommended aggregations for learning](https://www.tensorflow.org/federated/tutorials/tuning_recommended_aggregators) |\n\nThe created [`tff.templates.AggregationProcess`](../../tff/templates/AggregationProcess) zeroes out any values whose\nnorm is greater than that determined by the provided `zeroing_norm`, before\naggregating the values as specified by `inner_agg_factory`. Note that for\nweighted aggregation if some value is zeroed, the weight is unchanged. So for\nexample if you have a zeroed weighted mean and a lot of zeroing occurs, the\naverage will tend to be pulled toward zero. This is for consistency between\nweighted and unweighted aggregation\n\nThe provided `zeroing_norm` can either be a constant (for fixed norm), or an\ninstance of [`tff.templates.EstimationProcess`](../../tff/templates/EstimationProcess) (for adaptive norm). If it is an\nestimation process, the value returned by its `report` method will be used as\nthe zeroing norm. Its `next` method needs to accept a scalar float32 at\nclients, corresponding to the norm of value being aggregated. The process can\nthus adaptively determine the zeroing norm based on the set of aggregated\nvalues. For example if a [`tff.aggregators.PrivateQuantileEstimationProcess`](../../tff/aggregators/PrivateQuantileEstimationProcess) is\nused, the zeroing norm will be an estimate of a quantile of the norms of the\nvalues being aggregated.\n\nThe `value_type` provided to the `create` method must be a structure of\nfloats, but they do not all need to be the same, e.g. a mix of `np.float32`\nand `np.float16` dtypes is allowed.\n\nThe created process will report measurements\n\n- `zeroed_count`: The number of aggregands zeroed out.\n- `zeroing_norm`: The norm used to determine whether to zero out an aggregand.\n\nThe returned `AggregationFactory` takes its weightedness\n(`UnweightedAggregationFactory` vs. `WeightedAggregationFactory`) from\n`inner_agg_factory`.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `zeroing_norm` | Either a float (for fixed norm) or an `EstimationProcess` (for adaptive norm) that specifies the norm over which the values should be zeroed. |\n| `inner_agg_factory` | A factory specifying the type of aggregation to be done after zeroing. |\n| `norm_order` | A float for the order of the norm. Must be 1., 2., or infinity. |\n| `zeroed_count_sum_factory` | A factory specifying the type of aggregation done for `zeroed_count` measurement. If `None`, [`tff.aggregators.SumFactory`](../../tff/aggregators/SumFactory) will be used. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| An aggregation factory to perform zeroing. ||\n\n\u003cbr /\u003e"]]
View source on GitHub