  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.
