Infinity

class byzfl.Inf

Bases: object

Description

Generate extreme vector comprised of positive infinity values.

\[\begin{split}\mathrm{Inf}(x_1, \dots, x_n) = \begin{bmatrix} +\infty \\ +\infty \\ \vdots \\ +\infty \end{bmatrix} \in \mathbb{R}^d\end{split}\]

where

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

• \(d\) is the dimensionality of the input space, i.e., \(d\) is the number of coordinates of vectors \(x_1, \dots, x_n\).

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.Inf()

Using numpy arrays:

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

Using torch tensors:

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

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([inf, inf, inf])

Using list of torch tensors (Warning: We need the tensor to be either a floating point or complex dtype):

>>> 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([inf, inf, inf])