Flower is a Federated Learning framework that supports Secure Aggregation, where all clients' gradients are hidden. Yet, naive Contribution Evaluation schemes, such as Leave-1-Out and Include-1-In, still apply locally, even in this restricted setting. The student's task is to integrate these into Flower and test them exhaustively.