Evaluating Federated Learning for Cross-Country Mood Inference from Smartphone Sensing Data
This work addresses scalable and privacy-preserving mood inference for mental health monitoring, but it is incremental as it builds on existing federated learning methods with personalization.
The paper tackled mood inference from smartphone sensing data in a cross-country federated learning setting, introducing FedFAP, which achieved an AUROC of 0.744, outperforming centralized and personalized federated baselines.
Mood instability is a key behavioral indicator of mental health, yet traditional assessments rely on infrequent and retrospective reports that fail to capture its continuous nature. Smartphone-based mobile sensing enables passive, in-the-wild mood inference from everyday behaviors; however, deploying such systems at scale remains challenging due to privacy constraints, uneven sensing availability, and substantial variability in behavioral patterns. In this work, we study mood inference using smartphone sensing data in a cross-country federated learning setting, where each country participates as an independent client while retaining local data. We introduce FedFAP, a feature-aware personalized federated framework designed to accommodate heterogeneous sensing modalities across regions. Evaluations across geographically and culturally diverse populations show that FedFAP achieves an AUROC of 0.744, outperforming both centralized approaches and existing personalized federated baselines. Beyond inference, our results offer design insights for mood-aware systems, demonstrating how population-aware personalization and privacy-preserving learning can enable scalable and mood-aware mobile sensing technologies.