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.

翻译：情绪不稳定是心理健康的关键行为指标，然而传统评估依赖于不频繁且回顾性的报告，无法捕捉其连续特性。基于智能手机的移动感知能够从日常行为中实现被动的、真实环境下的情绪推断；然而，由于隐私限制、感知可用性不均以及行为模式的显著差异，大规模部署此类系统仍然具有挑战性。在本工作中，我们在跨国联邦学习环境中研究利用智能手机感知数据进行情绪推断，其中每个国家作为独立客户端参与，同时保留本地数据。我们提出了FedFAP，一个为适应跨地区异构感知模态而设计的特征感知个性化联邦框架。对地理和文化多样化人群的评估表明，FedFAP实现了0.744的AUROC，优于集中式方法和现有的个性化联邦基线。除了推断之外，我们的结果为情绪感知系统提供了设计见解，展示了如何通过群体感知的个性化与隐私保护学习，实现可扩展且具有情绪感知能力的移动感知技术。