Here below the glossary used for Fed-BioMed :

  • experiment : orchestrates the rounds during the federated learning, on the available nodes

    • it includes : training plan, model, federated trainer, training parameters, model parameters, set of input data, results
    • an experiment is unique (cannot be replayed) and is over when converged
    • status : running and then done
  • training : as commonly used in ML, process of feeding a model with data to improve its accuracy on some task.

  • validation : process of giving a heuristic information on the accuracy of a model during training.
  • testing : process of assessing the accuracy of a model after training, on holdout samples different from the one that were used for training. Not implemented yet in Fed-BioMed.

  • job : not a researcher notion. Interface between the researcher and the nodes of an experiment. It triggers the local work for all sampled nodes at each round.

  • round : everything included in choice of the nodes, perform local work on the nodes, sending back whatever information is required, server performs the aggregation
    • current Round() class on node corresponds to local work
  • parameter update : an update of the ML model parameters during the training loop, which usually corresponds to the processing of one batch of data
  • epoch : a number of parameter updates equivalent to processing the entire dataset exactly once

  • researcher (technical) : entity that defines and executes an experiment

  • node (technical) : entity with tagged datasets that replies to researcher queries and performs local work