Alzheimer's Disease (AD) and related dementia are a growing global health challenge due to the aging population. In this paper, we present ADMarker, the first end-to-end system that integrates multi-modal sensors and new federated learning algorithms for detecting multidimensional AD digital biomarkers in natural living environments. ADMarker features a novel three-stage multi-modal federated learning architecture that can accurately detect digital biomarkers in a privacy-preserving manner. Our approach collectively addresses several major real-world challenges, such as limited data labels, data heterogeneity, and limited computing resources. We built a compact multi-modality hardware system and deployed it in a four-week clinical trial involving 91 elderly participants. The results indicate that ADMarker can accurately detect a comprehensive set of digital biomarkers with up to 93.8% accuracy and identify early AD with an average of 88.9% accuracy. ADMarker offers a new platform that can allow AD clinicians to characterize and track the complex correlation between multidimensional interpretable digital biomarkers, demographic factors of patients, and AD diagnosis in a longitudinal manner.

ADMarker是一个集成了多模态传感器和新型联邦学习算法的端到端系统，用于在自然生活环境中检测多维度AD数字生物标记的首个系统。ADMarker采用了新颖的三阶段多模态联邦学习架构，能够以保护隐私的方式准确检测数字生物标记。通过一个持续四周的临床试验，ADMarker可以准确检测全面的数字生物标记，并具有高达93.8%的准确度和平均88.9%的早期AD检测准确度，为AD临床医生提供了一个新平台，可以纵向地描述和追踪多维可解释数字生物标记、患者人口统计学因素和AD诊断之间的复杂相关性。

ADMarker：一种用于监测阿尔茨海默病数字生物标志物的多模态联合学习系统