Federated Learning for Early Dropout Prediction on Healthy Ageing Applications

TL;DR

This paper introduces a federated learning approach for predicting early dropouts in healthy ageing applications, addressing privacy concerns and data fragmentation, and demonstrating improved accuracy over traditional methods.

Contribution

The paper proposes a novel federated machine learning framework tailored for healthy ageing applications, handling non-iid data and class imbalance without compromising privacy.

Findings

Federated learning achieves comparable or better accuracy than traditional ML.

Data selection and class imbalance techniques enhance model performance.

The approach effectively handles non-iid data in real-world scenarios.

Abstract

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.