* Iterators */

* Capacity */

* Capacity */

Resets the random seed Args: seed (int): the random seed

Returns the performance on the given dataset. Args: dataloader (torch.utils.data.DataLoader): a dataloader only_local

Add step information. Args: noise_params (dict): Parameters of the noise distribution. sampling_rate (float)

* Modifiers */

Get epsilon. Args: delta (float): Delta value. Returns: float: Epsilon value.

* @brief Recursively extract the vector of predictions of the training data * * @return vector<vector<float>> */

Reconstructs the images from the gradients received from the client Args: received_gradients: the list of gradients received from

* Iterators */

Train local models with the local datasets or the public dataset. Args: public (bool, optional): Train with the public dataset or

Add two numbers in the log space.

Reset step information.

Calculate the upper bound of RDP. Args: lam (float): Parameter lambda. steps_info (list): Information about

Calculates the accuracy of the model on the given dataloader Args: model (torch.nn.Module): model to be evaluated dataloader (tor