Federated Learning (FL) McMahan et al. [2017], a privacy-by-design machine learning approach, has introduced new ways to build recommender systems (RS). Unlike traditional approaches, the FL approach means that there is no longer a need to collect and store the users' private data on central servers, while making it possible to train robust recommendation models. In practice, FL distributes the model training process to the users' devices (i.e., the client or edge devices), thus allowing a global model to be trained using the user-specific local models. Each user updates the global model locally using their personal data and sends the local model updates to a server that aggregates them according to a pre-defined scheme. This is in order to update the global model. A prominent direction of research in this domain is based on Federated Collaborative Filtering (FCF) Ammad-Ud-Din et al. [2019], Chai et al. [2020], Dolui et al. [2019] that extends the standard Collaborative Filtering (CF) Hu et al. [2008] model to the federated mode. CF is one of the most frequently used matrix factorization models used to generate personalized recommendations either independently or in combination with other types of model Koren et al. [2009].