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[2602.15478] Evaluating Federated Learning for Cross-Country Mood Inference from Smartphone Sensing Data
Summary
This article evaluates a federated learning framework for mood inference using smartphone sensing data across different countries, highlighting its effectiveness and privacy benefits.
Why It Matters
The study addresses the challenge of mood instability assessment in mental health by leveraging smartphone data in a federated learning context. This approach preserves privacy while enabling scalable mood-aware systems, which is crucial for improving mental health monitoring and intervention strategies globally.
Key Takeaways
- Introduces FedFAP, a personalized federated learning framework for mood inference.
- Demonstrates improved performance in mood prediction across diverse populations.
- Highlights the importance of privacy-preserving techniques in mobile sensing.
- Offers insights for designing scalable mood-aware systems.
- Addresses challenges in deploying mood inference technologies globally.
Computer Science > Machine Learning arXiv:2602.15478 (cs) [Submitted on 17 Feb 2026] Title:Evaluating Federated Learning for Cross-Country Mood Inference from Smartphone Sensing Data Authors:Sharmad Kalpande, Saurabh Shirke, Haroon R. Lone View a PDF of the paper titled Evaluating Federated Learning for Cross-Country Mood Inference from Smartphone Sensing Data, by Sharmad Kalpande and 2 other authors View PDF HTML (experimental) Abstract: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 ...
