Determining the user's value within Federated Learning is not trivial, especially when their privacy is protected and the security of the model is guaranteed. The CrySyS lab developed a solution to tackle the former (privacy). However, more tests and experiments are needed to determine if it also applies to the latter (security). Furthermore, it is an open question of the usefulness of such information as contribution scores.