跨国家手机数据联合学习，实现隐私保护下的情绪持续监测。
Evaluating Federated Learning for Cross-Country Mood Inference from Smartphone Sensing Data
- 采用联邦学习框架，按国家分客户端本地训练。
- 跨区域测试下达到0.744 AUROC，优于集中式与基线方法。
- 适合关注心理健康监测与隐私保护的开发者与研究者。
情绪不稳定性是心理健康的关键行为指标，但传统评估依赖于低频且回顾性的报告，难以捕捉其连续性。基于智能手机的移动感知可从日常行为中被动推断情绪，但大规模部署面临隐私限制、传感设备分布不均及行为模式差异大等挑战。本文在跨国家联邦学习场景下研究情绪推断，每个国家作为独立客户端，本地保留数据。提出FedFAP——一种考虑异构传感模态的特征感知个性化联邦框架。在地理与文化差异显著的人群中评估，FedFAP取得0.744 AUROC，优于集中式方法及现有个性化联邦基线。结果不仅提供情绪推断性能提升，还为情绪感知系统设计提供洞见，表明人群感知的个性化与隐私保护学习可支持可扩展的情绪感知移动技术。
原文摘要 · Abstract (English)
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.
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