Simulate collaborative ML scenarios, experiment multi-partner learning approaches and measure respective contributions of different datasets to model performance.
Some contributivity measurement methods need to change some hyperparameters during the training ( for instance the weights of the aggregation, or, the list of partners with whom the training epoch will be made)
For now, it is done artificially by stopping the training, and re-run a new mpl, initialized with the model of the previous training, for one epoch.
One solution can be add an agent ( new class), which is passed as parameter to mpl. At each batch/epoch, the agent can be requested by mpl, and gives the new parameters.
Some contributivity measurement methods need to change some hyperparameters during the training ( for instance the weights of the aggregation, or, the list of partners with whom the training epoch will be made)
For now, it is done artificially by stopping the training, and re-run a new mpl, initialized with the model of the previous training, for one epoch.
One solution can be add an agent ( new class), which is passed as parameter to mpl. At each batch/epoch, the agent can be requested by mpl, and gives the new parameters.