diff --git a/docs/about/features_index/workflowinterface.rst b/docs/about/features_index/workflowinterface.rst
index 0c1e5e063b..66422d9122 100644
--- a/docs/about/features_index/workflowinterface.rst
+++ b/docs/about/features_index/workflowinterface.rst
@@ -167,7 +167,7 @@ A :code:`Runtime` defines where the flow will be executed, who the participants
 
     local_runtime = LocalRuntime(aggregator=aggregator, collaborators=collaborators, backend='single_process')
 
-Let's break this down, starting with the :code:`Aggregator` and :code:`Collaborator` components. These components represent the *Participants* in a Federated Learning experiment. Each participant has its own set of *private attributes* that represent the information / data specific to its role or requirements. As the name suggests these *private attributes* are accessible only to the particular participant, and are appropriately inserted into or filtered out of current Flow state when transferring from between Participants. For e.g. Collaborator private attributes are inserted into :code:`flow` when transitioning from Aggregator to Collaborator and are filtered out when transitioning from Collaborator to Aggregator.
+Let's break this down, starting with the :code:`Aggregator` and :code:`Collaborator` components. These components represent the *Participants* in a Federated Learning experiment. Each participant has its own set of *private attributes*. As the name suggests, these *private attributes* represent private information they do not want to share with others, and will be filtered out when there is a transition from the aggregator to the collaborator or vice versa. In the example above each collaborator has it's own `train_dataloader` and `test_dataloader` that are only available when that collaborator is performing it's tasks via `self.train_loader` and `self.test_loader`. Once those collaborators transition to a task at the aggregator, this private information is filtered out and the remaining collaborator state can safely be sent back to the aggregator. 
 
 In the above :code:`FederatedFlow`, each collaborator accesses train and test datasets via *private attributes* :code:`train_loader` and :code:`test_loader`. These *private attributes* need to be set in form of a dictionary(user defined), where the key is the name of the attribute and the value is the object. In this example :code:`collaborator.private_attributes` sets the collaborator *private attributes* :code:`train_loader` and :code:`test_loader` that are accessed by collaborator steps (:code:`aggregated_model_validation`, :code:`train` and :code:`local_model_validation`).