tff.learning.robust_aggregator | TensorFlow Federated (original) (raw)

tff.learning.robust_aggregator

Creates aggregator for mean with adaptive zeroing and clipping.

tff.learning.robust_aggregator(
    *,
    zeroing: bool = True,
    clipping: bool = True,
    weighted: bool = True,
    debug_measurements_fn: Optional[Callable[[tff.aggregators.AggregationFactory], tff.aggregators.AggregationFactory]] = None
) -> tff.aggregators.AggregationFactory

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.

Adaptive clipping approach is as described in Andrew, Thakkar et al. (2021)https://arxiv.org/abs/1905.03871, which the robust_aggregator applies without the addition of noise.

For details on clipping and zeroing see tff.aggregators.clipping_factoryand tff.aggregators.zeroing_factory. For details on the quantile-based adaptive algorithm see tff.aggregators.PrivateQuantileEstimationProcess.

Args
zeroing	Whether to enable adaptive zeroing for data corruption mitigation.
clipping	Whether to enable adaptive clipping in the L2 norm for robustness.
weighted	Whether the mean is weighted (vs. unweighted).
debug_measurements_fn	A callable to add measurements suitable for debugging learning algorithms. Often useful values include None,tff.learning.add_debug_measurements ortff.learning.add_debug_measurements_with_mixed_dtype.

Returns
A tff.aggregators.AggregationFactory.

Raises
TypeError	if debug_measurement_fn yields an aggregation factory whose weight type does not match weighted.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2024-09-20 UTC.