Serving personalized content on mobile devices has traditionally required pooling sensitive user data on centralized servers, a practice increasingly at odds with modern privacy expectations and geographical regulations. We present a two-stage federated recommendation system pipeline for mobile devices, built around a principled separation between non-sensitive user preference data and sensitive mobile context data that never leaves the device. The first stage runs a collaborative filtering model on non-sensitive app-context data in the cloud to generate a shortlist of relevant items. The second stage re-ranks these candidates on-device using sensitive mobile signals, with only model updates/gradients ever leaving the device. We validate the approach on MovieLens, UCI Human Activity Recognition, and a proprietary pilot dataset, and deliver a production-ready implementation as a Kotlin Multiplatform library deployable on Android and iOS.

翻译：在移动设备上提供个性化内容通常需要将敏感的用户数据汇集到中央服务器上，这种做法日益与现代隐私期望和地域法规相冲突。我们提出了一种适用于移动设备的两阶段联邦推荐系统流水线，其核心在于对非敏感的用户偏好数据与从不离开设备的敏感移动上下文数据进行原则性分离。第一阶段在云端对非敏感的应用上下文数据运行协同过滤模型，生成相关物品的候选短列表。第二阶段在设备端利用敏感的移动信号对这些候选项目进行重新排序，只有模型更新/梯度会离开设备。我们在MovieLens数据集、UCI人体活动识别数据集以及一个专有的试点数据集上验证了该方法，并提供了一个生产就绪的实现——一个可部署在Android和iOS上的Kotlin多平台库。