  While machine learning techniques are being applied to various fields for
their exceptional ability to find complex relations in large datasets, the
strengthening of regulations on data ownership and privacy is causing
increasing difficulty in its application to medical data. In light of this,
Federated Learning has recently been proposed as a solution to train on private
data without breach of confidentiality. This conservation of privacy is
particularly appealing in the field of healthcare, where patient data is highly
confidential. However, many studies have shown that its assumption of
Independent and Identically Distributed data is unrealistic for medical data.
In this paper, we propose Personalized Federated Cluster Models, a hierarchical
clustering-based FL process, to predict Major Depressive Disorder severity from
Heart Rate Variability. By allowing clients to receive more personalized model,
we address problems caused by non-IID data, showing an accuracy increase in
severity prediction. This increase in performance may be sufficient to use
Personalized Federated Cluster Models in many existing Federated Learning
scenarios.
