:py:mod:`kosmos.simulator.fl_simulator`
=======================================

.. py:module:: kosmos.simulator.fl_simulator







Classes
-------

.. py:class:: FLSimulator(network: kosmos.topology.net.Network, train_config: kosmos.ml.config.sl_train.FLTrainConfig, client_nodes: list[kosmos.topology.typing.NodeReference], server_node: kosmos.topology.typing.NodeReference, seed: int = 1)

   Bases: :py:class:`kosmos.simulator.simulator.Simulator`

   Federated learning simulator.

   Initialize the simulator.

   :param network: The network topology.
   :type network: Network
   :param train_config: The federated learning training configuration.
   :type train_config: FLTrainConfig
   :param client_nodes: The node references of the clients.
   :type client_nodes: list[NodeReference]
   :param server_node: The node reference of the server.
   :type server_node: NodeReference
   :param seed: The seed for the random number generator. Defaults to 1.
   :type seed: int





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   .. rubric:: Methods

   .. py:method:: train() -> collections.abc.Iterator[kosmos.ml.sl_result.SLTrainIterationResult]

      Run federated training across all configured rounds.

      :returns:

                An iterator yielding one training result per epoch
                                                  for all rounds.
      :rtype: Iterator[SLTrainIterationResult]


   .. py:method:: test() -> kosmos.ml.sl_result.SLTestIterationResult

      Evaluate the global model on the test dataset.

      :returns: The result of the global model evaluation.
      :rtype: SLTrainIterationResult