flip.nvflare.controllers.scatter_and_gather_ldm
===============================================

.. py:module:: flip.nvflare.controllers.scatter_and_gather_ldm


Classes
-------

.. autoapisummary::

   flip.nvflare.controllers.scatter_and_gather_ldm.ScatterAndGatherLDM


Module Contents
---------------

.. py:class:: ScatterAndGatherLDM(model_id: str = '', min_clients: int = 1, num_rounds_ae: int = 5, num_rounds_dm: int = 5, start_round: int = 0, model_locator_id='', wait_time_after_min_received: int = 10, aggregator_id=AppConstants.DEFAULT_AGGREGATOR_ID, persistor_id=AppConstants.DEFAULT_PERSISTOR_ID, shareable_generator_id=AppConstants.DEFAULT_SHAREABLE_GENERATOR_ID, train_task_name=AppConstants.TASK_TRAIN, train_timeout: int = 0, ignore_result_error: bool = True, fatal_error_delay: int = 5, task_check_period: float = 0.5, persist_every_n_rounds: int = 1)

   Bases: :py:obj:`nvflare.apis.impl.controller.Controller`


   
   The controller for FederatedAveraging Workflow.

   The ScatterAndGather workflow defines Federated training on all clients.
   The model persistor (persistor_id) is used to load the initial global model which is sent to all clients.
   Each clients sends it's updated weights after local training which is aggregated (aggregator_id). The
   shareable generator is used to convert the aggregated weights to shareable and shareable back to weights.
   The model_persistor also saves the model after training.

   :param model_id: ID of the model that the training is being performed under.
   :type model_id: str, required
   :param min_clients: Min number of clients in training. Defaults to 1.
   :type min_clients: int, optional
   :param num_rounds_ae: The total number of training rounds for autoencoder. Defaults to 5.
   :type num_rounds_ae: int, optional
   :param num_rounds_dm: The total number of training rounds for diffusion model. Defaults to 5.
   :type num_rounds_dm: int, optional
   :param start_round: Start round for training. Defaults to 0.
   :type start_round: int, optional
   :param model_locator_id: ID of the model locator component. Defaults to "".
   :type model_locator_id: str, optional
   :param wait_time_after_min_received: Time to wait before beginning aggregation after
                                        contributions received. Defaults to 10.
   :type wait_time_after_min_received: int, optional
   :param aggregator_id: ID of the aggregator component. Defaults to "aggregator".
   :type aggregator_id: str, optional
   :param persistor_id: ID of the persistor component. Defaults to "persistor".
   :type persistor_id: str, optional
   :param shareable_generator_id: ID of the shareable generator. Defaults to "shareable_generator".
   :type shareable_generator_id: str, optional
   :param train_task_name: Name of the train task. Defaults to "train".
   :type train_task_name: str, optional
   :param train_timeout: Time to wait for clients to do local training.
   :type train_timeout: int, optional
   :param ignore_result_error: whether this controller can proceed if result has errors. Defaults to
                               False.
   :type ignore_result_error: bool, optional
   :param fatal_error_delay: Time in seconds to delay before calling 'system_panic' if a task returns
                             an error result and ignore_result_error is set to false
   :type fatal_error_delay: int, optional
   :param task_check_period: interval for checking status of tasks. Defaults to 0.5.
   :type task_check_period: float, optional
   :param persist_every_n_rounds: persist the global model every n rounds. Defaults to 0.
                                  If n is 0 then no persist.
   :type persist_every_n_rounds: int, optional

   :raises TypeError: when any of input arguments does not have correct type
   :raises ValueError: when any of input arguments is out of range or are in an incorrect format


   .. py:attribute:: flip


   .. py:attribute:: model_id
      :value: ''



   .. py:attribute:: aggregator_id


   .. py:attribute:: persistor_id


   .. py:attribute:: model_locator_id
      :value: ''



   .. py:attribute:: shareable_generator_id


   .. py:attribute:: train_task_name


   .. py:attribute:: aggregator
      :value: None



   .. py:attribute:: persistor
      :value: None



   .. py:attribute:: shareable_gen
      :value: None



   .. py:attribute:: ignore_result_error
      :value: True



   .. py:method:: start_controller(fl_ctx: nvflare.apis.fl_context.FLContext) -> None


   .. py:method:: locate_server_models(fl_ctx: nvflare.apis.fl_context.FLContext) -> bool

      Locate server models for the current task.

      :param fl_ctx: _description_
      :type fl_ctx: FLContext

      :returns: *bool* -- _description_



   .. py:method:: control_flow(abort_signal: nvflare.apis.signal.Signal, fl_ctx: nvflare.apis.fl_context.FLContext) -> None


   .. py:method:: stop_controller(fl_ctx: nvflare.apis.fl_context.FLContext) -> None


   .. py:method:: handle_event(event_type: str, fl_ctx: nvflare.apis.fl_context.FLContext)


   .. py:method:: process_result_of_unknown_task(client: nvflare.apis.client.Client, task_name, client_task_id, result: nvflare.apis.shareable.Shareable, fl_ctx: nvflare.apis.fl_context.FLContext) -> None


   .. py:method:: get_persist_state(fl_ctx: nvflare.apis.fl_context.FLContext) -> dict


   .. py:method:: restore(state_data: dict, fl_ctx: nvflare.apis.fl_context.FLContext)