Sign Flipping#

class byzfl.SignFlipping[source]#

Bases: object

Description#

Send the opposite of the mean vector [1].

\[\mathrm{SignFlipping} \ (x_1, \dots, x_n) = - \frac{1}{n}\sum_{i=1}^{n} x_i\]

where

\(x_1, \dots, x_n\) are the input vectors, which conceptually correspond to correct gradients submitted by honest participants during a training iteration.

Initialization parameters:: None

Calling the instance

Input parameters:: vectors (numpy.ndarray, torch.Tensor, list of numpy.ndarray or list of torch.Tensor) – A set of vectors, matrix or tensors.
Returns:: numpy.ndarray or torch.Tensor – The data type of the output is the same as the input.

Examples

>>> import byzfl
>>> attack = byzfl.SignFlipping()

Using numpy arrays

>>> import numpy as np
>>> x = np.array([[1., 2., 3.],       # np.ndarray
>>>               [4., 5., 6.],
>>>               [7., 8., 9.]])
>>> attack(x)
array([-4. -5. -6.])

Using torch tensors

>>> import torch
>>> x = torch.tensor([[1., 2., 3.],   # torch.tensor
>>>                   [4., 5., 6.],
>>>                   [7., 8., 9.]])
>>> attack(x)
tensor([-4., -5., -6.])

Using list of numpy arrays

>>> import numpy as np
>>> x = [np.array([1., 2., 3.]),      # list of np.ndarray
>>>      np.array([4., 5., 6.]),
>>>      np.array([7., 8., 9.])]
>>> attack(x)
array([-4., -5., -6.])

Using list of torch tensors

>>> import torch
>>> x = [torch.tensor([1., 2., 3.]),  # list of torch.tensor
>>>      torch.tensor([4., 5., 6.]),
>>>      torch.tensor([7., 8., 9.])]
>>> attack(x)
tensor([-4., -5., -6.])

References