Bitfount-Supported Protocols

Protocols represent the agreed upon method by which a Data Scientist can perform machine learning tasks and are commonly used to control which model parameters a Data Scientist or Pod can capture upon task execution.

Bitfount currently supports the following protocols, which are described in more technical detail in the API Reference:

Task Permission	Description	When to Use
Federated Averaging	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 Data Scientist specifies.	When training or evaluating ML models in a federated manner. Works With: - Federated Training algorithm
Results Only	Returns the results from the provided algorithm. This protocol is the most permissive protocol and only involves one round of communication. It simply runs the algorithm on the specified Pod(s) and returns the results as a list (one element for every Pod).	To return the results of a task to the Data Scientist. Works With: - Column Average - Train and Evaluate - SQL Query - Private SQL Query algorithms
Private Set Intersection	Performs a private set intersection given the specified algorithm.	Private set intersections are best applied when performing a computation on encrypted data in order to mitigate the risk of data leakage. Works With: - Compute Intersection RSA algorithm

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