Adding persist_checkpoint flag to the plan

docs/about/features_index/taskrunner.rst

@@ -44,7 +44,8 @@ Configurable Settings
  - :code:`best_state_path`: (str:path) Defines the weight protobuf file path that will be saved to for the highest accuracy model during the experiment.
  - :code:`last_state_path`: (str:path)  Defines the weight protobuf file path that will be saved to during the last round completed in each experiment.
  - :code:`rounds_to_train`: (int) Specifies the number of rounds in a federation. A federated learning round is defined as one complete iteration when the collaborators train the model and send the updated model weights back to the aggregator to form a new global model. Within a round, collaborators can train the model for multiple iterations called epochs.
- - :code:`write_logs`: (boolean) Metric logging callback feature. By default, logging is done through `tensorboard <https://www.tensorflow.org/tensorboard/get_started>`_ but users can also use custom metric logging function for each task.      
+ - :code:`write_logs`: (boolean) Metric logging callback feature. By default, logging is done through `tensorboard <https://www.tensorflow.org/tensorboard/get_started>`_ but users can also use custom metric logging function for each task.     
+ - :code:`persist_checkpoint`: (boolean) Specifies whether to enable the storage of a persistent checkpoint in non-volatile storage for recovery purposes. When enabled, the aggregator will restore its state to what it was prior to the restart, ensuring continuity after a restart. 
 
 
 - :class:`Collaborator <openfl.component.Collaborator>`

openfl-workspace/workspace/plan/defaults/aggregator.yaml

@@ -1,3 +1,4 @@
 template : openfl.component.Aggregator
 settings :
     db_store_rounds : 2
+    persist_checkpoint: True