This function returns a dict, where the key is metric name, and value is \ the function of how to calculate the metric and a bool to indicate

Build metrics for evaluation. Args: self: write your description eval_metric: write your description

Return the MF model according to model configs Arguments: model_config: the model related parameters data_shape (int): the input

return multiple model parameters :return:

Transfer the model parameter updates to gradient based on: .. math:: P_{t} = P - \eta g, where :math:`P

Args: dataset_name (str): dataset name :returns: the auxiliary dataset for property inference attack. Type: dict {

Computes the exact and f1 scores from the examples and the model predictions

Args: atk_method: the attack method name, and currently supporting "DLG: deep leakage from gradient", and "IG: Inverting gradien

To instantiate a scheduler object for conducting HPO Arguments: init_cfg: configuration client_cfgs: client-specific configuration

Extract the input shape from the given data, which can be used to build \ the data. Users can also use `data.input_shape` to specify the shap

r""" Returns: the homophily for the raw G and split G

Using iDLG trick to recover the label. Paper: "iDLG: Improved Deep Leakage from Gradients", link: https://arxiv.org/abs/2001.02610 Args:

init necessary attributes used in Ditto, `global_model` acts as the shared global model in FedAvg; `local_model` acts as personalized mod

Set proximal regularizer and the factor of regularizer

This function sets the default config value. (1) Note that for an experiment, only part of the arguments will be used \ The remaining un

init multiple models and optimizers: the default implementation is copy init manner; ========================= Ex

Set proximal regularizer, and the scale of gaussian noise

init necessary attributes used in pFedMe, some new attributes will be with prefix `pFedMe` optimizer to avoid namespace pollution No

Init necessary attributes used in SGDMF, some new attributes will be with prefix `SGDMF` optimizer to avoid namespace pollution

A fixed latency for every communication. From: Wang et al. “A field guide to federated optimization”.

Return the dataset of ``femnist`` or ``celeba``. Args: config: configurations for FL, see ``federatedscope.core.configs`` Retur

Loads the dataset for the given config from branches Args: config: configurations for FL, see ``federatedscope.core.configs`` N

Based on the configuration file, this function imports external \ datasets and applies train/valid/test. Args: config: `CN` from

r"""Convert dataset to Dataloader. :returns: data_local_dict :rtype: Dict { 'client_id': { 't

r""" :returns: data_dict :rtype: (Dict): dict{'client_id': Data()}

Return the dataset of matrix factorization Format: { 'client_id': { 'train': DataLoader(), 't

Return the dataset of ``shakespeare``, ``subreddit``, ``twitter``, \ or ``synthetic``. Args: config: configurations for FL, see

r""" :returns: data_dict :rtype: Dict: dict{'client_id': Data()}

Load the synthetic data for contrastive learning

To load data for vertical FL Arguments: config: configuration generate (bool): whether to generate the synthetic data :r

parse the logfiles [exp_print.log, eval_results.log] into wandb_dict that contains non-nested dicts :param exp_log_f: opened exp

Merge `cfg_other` into this CfgNode.