tff.learning.ddp_secure_aggregator

Creates aggregator with adaptive zeroing and distributed DP.

tff.learning.ddp_secure_aggregator(
    noise_multiplier: float,
    expected_clients_per_round: int,
    bits: int = 20,
    zeroing: bool = True,
    rotation_type: str = 'hd'
) -> tff.aggregators.UnweightedAggregationFactory

Zeroes out extremely large values for robustness to data corruption on clients, and performs distributed DP (compression, discrete noising, and SecAgg) with adaptive clipping for differentially private learning. For details of the two main distributed DP algorithms see https://arxiv.org/pdf/2102.06387 or https://arxiv.org/pdf/2110.04995.pdf The adaptive clipping uses the geometric method described in https://arxiv.org/abs/1905.03871

noise_multiplier A float specifying the noise multiplier (with respect to the initial L2 cipping) for the distributed DP mechanism for model updates. A value of 1.0 or higher may be needed for meaningful privacy.
expected_clients_per_round An integer specifying the expected number of clients per round. Must be positive.
bits An integer specifying the bit-width for the aggregation. Note that this is for the noisy, quantized aggregate at the server and thus should account for the expected_clients_per_round. Must be in the inclusive range of [1, 22]. This is set to 20 bits by default, and it dictates the computational and communication efficiency of Secure Aggregation. Setting it to less than 20 bits should work fine for most cases. For instance, for an expected number of securely aggregated client updates of 100, 12 bits should be enough, and for an expected number of securely aggregated client updates of 1000, 16 bits should be enough.
zeroing A bool indicating whether to enable adaptive zeroing for data corruption mitigation. Defaults to True.
rotation_type A string indicating what rotation to use for distributed DP. Valid options are 'hd' (Hadamard transform) and 'dft' (discrete Fourier transform). Defaults to hd.

A tff.aggregators.UnweightedAggregationFactory.