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federated_averaging

Federated Averaging protocol.

Classes​

FederatedAveraging​

class FederatedAveraging(    *,    algorithm: _FederatedAveragingCompatibleAlgoFactory,    aggregator: Optional[_BaseAggregatorFactory] = None,    steps_between_parameter_updates: Optional[int] = None,    epochs_between_parameter_updates: Optional[int] = None,    auto_eval: bool = True,    secure_aggregation: bool = False,    **kwargs: Any,):

Original Federated Averaging algorithm by McMahan et al. (2017).

This protocol performs a predetermined number of epochs or steps of training on each remote Pod before sending the updated model parameters to the modeller. These parameters are then averaged and sent back to the Pods for as many federated iterations as the Modeller specifies.

Arguments

  • algorithm: The algorithm to use for training. This must be compatible with the FederatedAveraging protocol.
  • aggregator: The aggregator to use for updating the model parameters across all Pods participating in the task. This argument takes priority over the secure_aggregation argument.
  • steps_between_parameter_updates: The number of steps between parameter updates, i.e. the number of rounds of local training before parameters are updated. If epochs_between_parameter_updates is provided, steps_between_parameter_updates cannot be provided. Defaults to None.
  • epochs_between_parameter_updates: The number of epochs between parameter updates, i.e. the number of rounds of local training before parameters are updated. If steps_between_parameter_updates is provided, epochs_between_parameter_updates cannot be provided. Defaults to None.
  • auto_eval: Whether to automatically evaluate the model on the validation dataset. Defaults to True.
  • secure_aggregation: Whether to use secure aggregation. This argument is overridden by the aggregator argument.

Attributes

  • name: The name of the protocol.
  • algorithm: The algorithm to use for training
  • aggregator: The aggregator to use for updating the model parameters.
  • steps_between_parameter_updates: The number of steps between parameter updates.
  • epochs_between_parameter_updates: The number of epochs between parameter updates.
  • auto_eval: Whether to automatically evaluate the model on the validation dataset.

Raises

  • TypeError: If the algorithm is not compatible with the protocol.
tip

For more information, take a look at the seminal paper: https://arxiv.org/abs/1602.05629

Ancestors​

  • bitfount.federated.protocols.base._BaseProtocolFactory
  • abc.ABC
  • bitfount.federated.roles._RolesMixIn
  • bitfount.types._BaseSerializableObjectMixIn

Methods​

def dump(self) ‑> SerializedProtocol:

Returns the JSON-serializable representation of the protocol.

def modeller(    self,    mailbox: _ModellerMailbox,    early_stopping: Optional[FederatedEarlyStopping] = None,    **kwargs: Any,) ‑> bitfount.federated.protocols.model_protocols.federated_averaging._ModellerSide:

Returns the modeller side of the FederatedAveraging protocol.

def worker(    self, mailbox: _WorkerMailbox, hub: BitfountHub, **kwargs: Any,) ‑> bitfount.federated.protocols.model_protocols.federated_averaging._WorkerSide:

Returns the worker side of the FederatedAveraging protocol.

Raises

  • TypeError: If the mailbox is not compatible with the aggregator.

Variables​

  • static algorithm : bitfount.federated.protocols.model_protocols.federated_averaging._FederatedAveragingCompatibleAlgoFactory
  • static nested_fields : ClassVar[Dict[str, Mapping[str, Any]]]