# ADMarker: A Multi-Modal Federated Learning System for Monitoring Digital Biomarkers of Alzheimer's Disease

arXiv: https://arxiv.org/abs/2310.15301 · HTML: https://mlsanity.com/paper/2310.15301/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 67 references that are in the map.
- First posted on arXiv 23 Oct 2023.

## Builds on (most cited first, 6 of 6)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [What Makes for Good Views for Contrastive Learning?](https://mlsanity.com/paper/2005.10243/) (2020)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [3DV: 3D Dynamic Voxel for Action Recognition in Depth Video](https://mlsanity.com/paper/2005.05501/) (2020)

## Built on by (most cited first, 14 of 14)

- [AutoIOT: LLM-Driven Automated Natural Language Programming for AIoT Applications](https://mlsanity.com/paper/2503.05346/) (2025)
- [GPIoT: Tailoring Small Language Models for IoT Program Synthesis and Development](https://mlsanity.com/paper/2503.00686/) (2025)
- [MMBind: Unleashing the Potential of Distributed and Heterogeneous Data for Multimodal Learning in IoT](https://mlsanity.com/paper/2411.12126/) (2024)
- [Federated Learning for Time-Series Healthcare Sensing with Incomplete Modalities](https://mlsanity.com/paper/2405.11828/) (2024)
- [Towards Edge General Intelligence via Large Language Models: Opportunities and Challenges](https://mlsanity.com/paper/2410.18125/) (2024)
- [MMEdge: Accelerating On-device Multimodal Inference via Pipelined Sensing and Encoding](https://mlsanity.com/paper/2510.25327/) (2025)
- [Tazza: Shuffling Neural Network Parameters for Secure and Private Federated Learning](https://mlsanity.com/paper/2412.07454/) (2024)
- [FlexLoc: Conditional Neural Networks for Zero-Shot Sensor Perspective Invariance in Object Localization with Distributed Multimodal Sensors](https://mlsanity.com/paper/2406.06796/) (2024)
- [MoViD: View-Invariant 3D Human Pose Estimation via Motion-View Disentanglement](https://mlsanity.com/paper/2604.03299/) (2026)
- [Toward Storage-Aware Learning with Compressed Data An Empirical Exploratory Study on JPEG](https://mlsanity.com/paper/2508.12833/) (2025)
- [Mix-modal Federated Learning for MRI Image Segmentation](https://mlsanity.com/paper/2509.02541/) (2025)
- [FM-FoG: A Real-Time Foundation Model-based Wearable System for Freezing-of-Gait Mitigation](https://mlsanity.com/paper/2509.24176/) (2025)
- [NeuroPath: Practically Adopting Motor Imagery Decoding through EEG Signals](https://mlsanity.com/paper/2604.09654/) (2026)
- [A Multimodal Framework for Dementia Detection via Linguistic and Acoustic Representation Learning](https://mlsanity.com/paper/2605.25540/) (2026)

## Abstract (arXiv)

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.