Federated learning is a machine learning approach that allows different clients to collaboratively train a common model without sharing their data sets. Since clients have different data and classify data differently, there is a trade-off between the generality of the common model and the personalization of the classification results. Current approaches rely on using a combination of a global model, common to all clients, and multiple local models, that support personalization. In this paper, we report the results of a study, where we have applied some of these approaches to a concrete use case, namely the Anonymous platform from Anonymous Company, where Graph Neural Networks help programmers in the development of applications. Our results show that the amount of data points of each client affects the personalization strategy and that there is no optimal strategy that fits all clients.