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