|---| | [ 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) | Creates an aggregation factory to perform L2 clipping. tff.aggregators.clipping_factory( clipping_norm: Union[float, https://www.tensorflow.org/federated/api_docs/python/tff/templates/EstimationProcess], inner_agg_factory: https://www.tensorflow.org/federated/api_docs/python/tff/aggregators/AggregationFactory, clipped_count_sum_factory: Optional[https://www.tensorflow.org/federated/api_docs/python/tff/aggregators/UnweightedAggregationFactory] = None ) -> https://www.tensorflow.org/federated/api_docs/python/tff/aggregators/AggregationFactory ### Used in the notebooks | Used in the tutorials | |---| | - [Tuning recommended aggregations for learning](https://www.tensorflow.org/federated/tutorials/tuning_recommended_aggregators) | The created [`tff.templates.AggregationProcess`](https://www.tensorflow.org/federated/api_docs/python/tff/templates/AggregationProcess) projects the values onto an L2 ball (also referred to as "clipping") with norm determined by the provided `clipping_norm`, before aggregating the values as specified by `inner_agg_factory`. The provided `clipping_norm` can either be a constant (for fixed norm), or an instance of [`tff.templates.EstimationProcess`](https://www.tensorflow.org/federated/api_docs/python/tff/templates/EstimationProcess) (for adaptive norm). If it is an estimation process, the value returned by its `report` method will be used as the clipping 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 clipping norm based on the set of aggregated values. For example if a [`tff.aggregators.PrivateQuantileEstimationProcess`](https://www.tensorflow.org/federated/api_docs/python/tff/aggregators/PrivateQuantileEstimationProcess) is used, the clip 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 - `clipped_count`: The number of aggregands clipped. - `clipping_norm`: The norm used to determine whether to clip an aggregand. The returned `AggregationFactory` takes its weightedness (`UnweightedAggregationFactory` vs. `WeightedAggregationFactory`) from `inner_agg_factory`.


| ## Args || |---|---| | `clipping_norm` | Either a float (for fixed norm) or an `EstimationProcess` (for adaptive norm) that specifies the norm over which the values should be clipped. | | `inner_agg_factory` | A factory specifying the type of aggregation to be done after clipping. | | `clipped_count_sum_factory` | A factory specifying the type of aggregation done for `clipped_count` measurement. If `None`, [`tff.aggregators.SumFactory`](https://www.tensorflow.org/federated/api_docs/python/tff/aggregators/SumFactory) will be used. |



| ## Returns || |---|---| | An aggregation factory to perform L2 clipping. ||