flox.logic.base_client.FloxClientLogic¶

class FloxClientLogic[source]¶

Abstract base class for FLoX Clients

Methods

`on_data_retrieve`	Retrieves data for training the model.
`on_model_fit`	Fit the provided global model using a local dataset
`on_model_receive`	Parses & decrypts the received data from a controller
`on_model_send`	Final data processing before sending the results back, such as encryption.
`run_round`	Combines the rest of the functions to run a single Federated Learning round

on_data_retrieve(config)[source]¶

Retrieves data for training the model.

Parameters¶

config: ConfigFile: a dictionary with values necessary to retrieve the data, such as the data source and preprocessing parameters

XYData: data for evaluating the model. This can take different forms depending on the ML framework you use. For Tensorflow, it would look as x_test and y_test, while for PyTorch, it would look as a single DataLoader instance.

on_model_fit()[source]¶

Fit the provided global model using a local dataset

Parameters for fitting the model. This might take different forms depending on the ML framework. For Tensorflow, it might look like this:

model: keras.Sequential ModelTrainer: Class config: ConfigFile x_train: NDArrays y_train: Union[NDArray, NDArrays]

on_model_receive()[source]¶

Parses & decrypts the received data from a controller

on_model_send()[source]¶

Final data processing before sending the results back, such as encryption.

results: dict

FL results formatted as a dictionary. For example: results = {

“model_weights”: model_weights, “samples_count”: samples_count, “bias_weights”: fractions,

}

run_round(model_trainer, config)[source]¶

Combines the rest of the functions to run a single Federated Learning round

model_trainer: instance of a class based on BaseModelTrainer that implements .fit(), .evaluate(), .get_weights(), and .set_weights()
config: dict: a dictionary with all the necessary parameters for retrieving data and training the model

results: dict

FL results formatted as a dictionary. For example: results = {

“model_weights”: model_weights, “samples_count”: samples_count, “bias_weights”: fractions,

}