In federated learning, model updates need to be serialized and deserialized in order to be transferred between clients and server/combiner. There is also a need to write and load models to/from disk, for example to transiently store updates during training rounds. Furthermore, aggregation algorithms need to perform a range of numerical operations on the model updates (addition, multiplication, etc). Since different ML frameworks (TF, Torch, etc) have different internal ways to represent model parameters, there is a need to inform the framework how to handle models of a given type. In FEDn, this compatibility layer is the task of Helpers.
A helper is defined by the interface in :py:mod:`fedn.utils.helpers.helperbase.HelperBase`. By implementing a helper plugin, a developer can extend the framework with support for new ML frameworks and numerical operations.
FEDn ships with a default helper implementation, numpyhelper.
This helper relies on the assumption that the model update is made up of parameters
represented by a list of :py:class:`numpy.ndarray` arrays. Since most ML frameworks have
good numpy support it should in most cases be sufficient to use this helper.
Both TF/Keras and PyTorch models can be readily serialized in this way.
To add a helper plugin “myhelper” you implement the interface and place a file called ‘myhelper.py’ in the folder fedn.utils.helpers.plugins.
See the Keras and PyTorch quickstart examples and :py:mod:`fedn.utils.helpers.plugins.numpyhelper` for further details.