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federated_training

Algorithm to train a model remotely and return its parameters.

Classes​

FederatedModelTraining​

class FederatedModelTraining(*, model: _DistributedModelTypeOrReference, **kwargs: Any):

Algorithm for training a model remotely and returning its updated parameters.

This algorithm is designed to be compatible with the FederatedAveraging protocol.

Arguments

  • model: The model to train on remote data.

Attributes

  • name: The name of the algorithm.
  • model: The model to train on remote data.

Ancestors​

  • bitfount.federated.algorithms.model_algorithms.base._BaseModelAlgorithmFactory
  • bitfount.federated.algorithms.base._BaseAlgorithmFactory
  • abc.ABC
  • bitfount.federated.roles._RolesMixIn
  • bitfount.types._BaseSerializableObjectMixIn

Methods​

def modeller(    self, **kwargs: Any,) ‑> bitfount.federated.algorithms.model_algorithms.federated_training._ModellerSide:

Returns the modeller side of the FederatedModelTraining algorithm.

def worker(    self, hub: BitfountHub, **kwargs: Any,) ‑> bitfount.federated.algorithms.model_algorithms.federated_training._WorkerSide:

Returns the worker side of the FederatedModelTraining algorithm.

Arguments

  • hub: BitfountHub object to use for communication with the hub.

Variables​

  • static nested_fields : ClassVar[Dict[str, Mapping[str, Any]]]