Plugin Components

Plugin Components#

Open Federated Learning (OpenFL) is designed to be a flexible and extensible framework. Plugins are interchangeable parts of OpenFL components. Different plugins support varying usage scenarios. A plugin may be required or optional.

You can provide your implementations of OpenFL plugins to achieve a desired behavior. Technically, a plugin is just a class that implements some interface. You may enable a plugin by putting its import path and initialization parameters to the config file of a corresponding OpenFL component or to the frontend Python API. See openfl-tutorials for more details.

Framework Adapter#

The Framework Adapter plugin enables OpenFL support for Deep Learning frameworks usage in FL experiments. It is a required plugin for the frontend API component and Envoy. All the framework-specific operations on model weights are isolated in this plugin so OpenFL can be framework-agnostic.

The Framework adapter plugin interface has two required methods to load and extract tensors from a model and an optimizer:

  • get_tensor_dict

  • set_tensor_dict

get_tensor_dict method accepts a model and optionally an optimizer. It should return a dictionary {tensor_name : ndarray} that maps tensor names to tensors in the NumPy representation.

@staticmethod
def get_tensor_dict(model, optimizer=None) -> dict:

set_tensor_dict method accepts a tensor dictionary, a model, and optionally an optimizer. It loads weights from the tensor dictionary to the model in place. Tensor names in the dictionary match corresponding names set in get_tensor_dict.

@staticmethod
def set_tensor_dict(model, tensor_dict, optimizer=None, device='cpu') -> None:

If your new framework model cannot be directly serialized with pickle-type libraries, you can optionally implement the serialization_setup method to prepare the model object for serialization.

def serialization_setup():
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