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Creates aggregator with compression and adaptive zeroing and clipping.

Zeroes out extremely large values for robustness to data corruption on clients and clips in the L2 norm to moderately high norm for robustness to outliers. After weighting in mean, the weighted values are uniformly quantized to reduce the size of the model update communicated from clients to the server. For details, see Suresh et al. (2017) The default configuration is chosen such that compression does not have adverse effect on trained model quality in typical tasks.

zeroing Whether to enable adaptive zeroing for data corruption mitigation.
clipping Whether to enable adaptive clipping in the L2 norm for robustness. Note this clipping is performed prior to the per-coordinate clipping required for quantization.
weighted Whether the mean is weighted (vs. unweighted).
add_debug_measurements Whether to add measurements suitable for debugging learning algorithms. For more detail on these measurements, see tff.learning.add_debug_measurements.

A tff.aggregators.AggregationFactory.