  The provision of social care applications is crucial for elderly people to
improve their quality of life and enables operators to provide early
interventions. Accurate predictions of user dropouts in healthy ageing
applications are essential since they are directly related to individual health
statuses. Machine Learning (ML) algorithms have enabled highly accurate
predictions, outperforming traditional statistical methods that struggle to
cope with individual patterns. However, ML requires a substantial amount of
data for training, which is challenging due to the presence of personal
identifiable information (PII) and the fragmentation posed by regulations. In
this paper, we present a federated machine learning (FML) approach that
minimizes privacy concerns and enables distributed training, without
transferring individual data. We employ collaborative training by considering
individuals and organizations under FML, which models both cross-device and
cross-silo learning scenarios. Our approach is evaluated on a real-world
dataset with non-independent and identically distributed (non-iid) data among
clients, class imbalance and label ambiguity. Our results show that data
selection and class imbalance handling techniques significantly improve the
predictive accuracy of models trained under FML, demonstrating comparable or
superior predictive performance than traditional ML models.
