computes DTW distances for gcfl+

Arguments: config (dict): key is cfg node name, value is the specified value. Returns: results (list): cmd args

Arguments: config (dict): key is cfg node name, value is the choice of hyper-parameter. Returns: name (str): the stri

Takes a list of reference sentences for a single segment and returns an object that encapsulates everything that BLEU needs to know about

creating the poisoned FEMNIST dataset with edge-case triggers we are going to label 7s from the ARDIS dataset as 1 (dirty label) load the

Create FS server session and store args in Redis.

Served as an example, we discount the model update with staleness tau \ as: ``(1.0/((1.0+\tau)**factor))``, \ which has been

Discretize a given continuous search space into the given number of buckets. Arguments: contd_choices (Continuous): continuous choices.

Display all the joinable FS tasks.

Run ROUGE to evaluate the system summaries in system_dir against the model summaries in model_dir. The summaries are assumed to

Arguments: param_name (str): parameter name. Returns: preserve (bool): whether to preserve this parameter.

r""" return dict{ID:[idxs]}

r""" return dict{ID:[idxs]}

Save the generated data and the generator training loss

Get the generator loss based on the discriminator's output Args: discriminator_output (Tensor): the discriminator's outp

This function builds an aggregator, which is a protocol for aggregate \ all clients' model(s). Arguments: method: key to determi

Interface to the get_configuration_space function from the FEMNIST Benchmark. Parameters ---------- seed : in

Interface to the get_configuration_space function from the FEMNIST Benchmark. Parameters ---------- seed : in

r""" This function returns a function of cost model. :param key: name of cost model. :return: the function of cost model

Project the tokenized prediction back to the original text.

Get the dataset's corresponding generator. Args: dataset_name: The dataset name; Type: str :returns: The generator; Type

This function is borrowed from offsite-tuning: https://github.com/mit-han-lab/offsite-tuning/blob/main/offsite_tuning /utils.py

Modified from the official implementation: https://github.com/mit-han-lab/offsite-tuning/tree/main