Fedbiomed Main components

Fed-BioMed has two components ensuring the correct execution of collaborative learning algorithms. These components are Node and Researcher, and are defined in the following sections:


In Fed-BioMed, Node provides 2 main functionalities:

  • Node stores datasets, upon which the Federated Learning Model will be trained and Federated Analytics will be performed. Datasets paths towards files and folders in a TinyDB database, as well as other metadata such as datatype, data shape, ... .
  • Node trains a model upon a TrainRequest (sent by Researcher), and send it back to the Researcher once training is completed.

Node is a client that is responsible for sending replies in response to Researcher requests. Since Node is not a server, communication between node and researcher is provided through Network component. When a node is started,
it directly connects to the Network component.

More details about Node installation can be found here.


In Fed-BioMed, a Researcher is an entity that orchestrates federated workflow among the nodes. It is the server (gRPC) that each node participating federated experiment is connected. Researcher component is also the entity that the end-user connect and uses for federated experiment. It provides Python APIs to define the experiment and all the elements needed for an experiment. In order to define an experiment, user has to provide 3 elements using Researcher component:

  • a TrainingPlan(containing a model, method to load/pre-process data and dependencies)
  • a Strategy, which defines how nodes are selected / sampled while training a Federated Mode
  • an Aggregator, which purpose is to aggregate the local model coming from each node into an aggregated model. Aggregation is performed on Researcher side. Researcher can be run using plain Python scripts or Jupyter Notebook (thus in an interactive fashion).

Researcher orchestrates the training by submitting training tasks to Nodes. Connected nodes subscribe the submitted tasks/request through RPC calls. After the task execution is completed Node answers back with an appropriate reply for each task submitted and subscribed by them.

More details about Researcher and its installation can be found here.

Fed-BioMed Architecture

Relationship between each main component aforementioned is detailed in the figure below:

alt text Fed-BioMed basic architecture

As shown in the diagram, Researcher is a central component in Fed-BioMed that submits federated queries (model training, federated analytics etc.) to the nodes and, collects the results. The network infrastructure is based on nodes collecting tasks from researcher rather than researcher sending requests to the nodes. The tasks created by end-user is stored by Researcher and submitted to nodes once they send task-collect request to the Researcher component. Nodes are in charge of running the model training or executing any other tasks that is submitted by the Researcher, and sending the results back to the Researcher by creating a reply request. Large files such as model parameters are exchanged using streaming to avoid memory issues.

Node configuration

For information on how to configure Node, please follow Node configuration steps

Researcher configuration

For information on how to configure Researcher, please follow Researcher configuration steps