Federated Learning for Early Dropout Prediction on Healthy Ageing Applications
This work addresses privacy and data fragmentation issues in predicting dropouts for elderly users of social care applications, representing an incremental improvement by applying federated learning to a specific domain.
The paper tackled the problem of predicting user dropouts in healthy ageing applications by proposing a federated machine learning approach that addresses privacy concerns and data fragmentation, achieving comparable or superior predictive accuracy to traditional methods on a real-world dataset with non-iid data, class imbalance, and label ambiguity.
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.