capacity: int = 1000,
max_string_length: int = 10,
repetitions: int = 3,
seed: int = 0,
dtype: tf.dtypes.DType = tf.int64,
max_heavy_hitters: Optional[int] = None,
max_words_per_user: Optional[int] = None,
k_anonymity: int = 1,
secure_sum_bitwidth: Optional[int] = None,
batch_size: int = 1,
multi_contribution: bool = True,
decode_iblt_fn: Optional[Callable[..., Tuple[tf.Tensor, tf.Tensor, tf.Tensor]]] = None
) -> tff.Computation
The capacity of the IBLT sketch. Defaults to 1000.
The maximum length of a string in the IBLT. Defaults to
10. Must be positive.
The number of repetitions in IBLT data structure (must be >=
3). Defaults to 3. Must be at least 3.
An integer seed for hash functions. Defaults to 0.
A tensorflow data type which determines the type of the IBLT values.
Must be tf.int32 or tf.int64. Defaults to tf.int64.
The maximum number of items to return. If the decoded
results have more than this number of items, will order decreasingly by
the estimated counts and return the top max_heavy_hitters items. Default
max_heavy_hitters == None, which means to return all the heavy hitters
in the result.
The maximum number of words each client is allowed to
contribute. If not None, must be a positive integer. Defaults to None,
which means all the clients contribute all their words.
Only return words that appear in at least k clients. Must be a
positive integer. Defaults to 1.
The bitwidth used for secure sum. The default value is
None, which disables secure sum. If not None, must be in the range
[1,62]. See tff.federated_secure_sum_bitwidth.
The number of elements in each batch of the dataset. Defaults
to 1, means the input dataset is processed by
tf.data.Dataset.batch(1). Must be a positive.
Whether each client is allowed to contribute multiple
counts or only a count of one for each unique word. Defaults to True.
A function to decode key-value pairs from an IBLT sketch.
Defaults to None, in this case decode_iblt_fn will be set to