# Personalized Digital Health Modeling with Adaptive Support Users

arXiv: https://arxiv.org/abs/2605.02004 · HTML: https://mlsanity.com/paper/2605.02004/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 38 references that are in the map.
- First posted on arXiv 3 May 2026.

## Builds on (most cited first, 23 of 23)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [FedBN: Federated Learning on Non-IID Features via Local Batch Normalization](https://mlsanity.com/paper/2102.07623/) (2021)
- [Adaptive Personalized Federated Learning](https://mlsanity.com/paper/2003.13461/) (2020)
- [Personalized Federated Learning: A Meta-Learning Approach](https://mlsanity.com/paper/2002.07948/) (2020)
- [Multitask learning and benchmarking with clinical time series data](https://mlsanity.com/paper/1703.07771/) (2017)
- [Personalized Cross-Silo Federated Learning on Non-IID Data](https://mlsanity.com/paper/2007.03797/) (2020)
- [Personalized Federated Learning with First Order Model Optimization](https://mlsanity.com/paper/2012.08565/) (2020)
- [FedBABU: Towards Enhanced Representation for Federated Image Classification](https://mlsanity.com/paper/2106.06042/) (2021)
- [Federated Meta-Learning with Fast Convergence and Efficient Communication](https://mlsanity.com/paper/1802.07876/) (2018)
- [GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization](https://mlsanity.com/paper/2211.02733/) (2022)
- [ChatDiet: Empowering Personalized Nutrition-Oriented Food Recommender Chatbots through an LLM-Augmented Framework](https://mlsanity.com/paper/2403.00781/) (2024)
- [GPFedRec: Graph-guided Personalization for Federated Recommendation](https://mlsanity.com/paper/2305.07866/) (2023)
- [Connecting Low-Loss Subspace for Personalized Federated Learning](https://mlsanity.com/paper/2109.07628/) (2021)
- [Personalized Stress Monitoring using Wearable Sensors in Everyday Settings](https://mlsanity.com/paper/2108.00144/) (2021)
- [Auxo: Efficient Federated Learning via Scalable Client Clustering](https://mlsanity.com/paper/2210.16656/) (2022)
- [Integrating Wearable Sensor Data and Self-reported Diaries for Personalized Affect Forecasting](https://mlsanity.com/paper/2403.13841/) (2024)
- [Personalized Federated Learning with Attention-based Client Selection](https://mlsanity.com/paper/2312.15148/) (2023)
- [The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning](https://mlsanity.com/paper/2407.15464/) (2024)
- [Loneliness Forecasting Using Multi-modal Wearable and Mobile Sensing in Everyday Settings](https://mlsanity.com/paper/2410.00020/) (2024)
- [Objective Prediction of Tomorrow's Affect Using Multi-Modal Physiological Data and Personal Chronicles: A Study of Monitoring College Student Well-being in 2020](https://mlsanity.com/paper/2201.11230/) (2022)

## Abstract (arXiv)

Personalized models are essential in digital health because individuals exhibit substantial physiological and behavioral heterogeneity. Yet personalization is limited by scarce and noisy user-specific data. Most existing methods rely on population pretraining or data from similar users only, which can lead to biased transfer and weak generalization. We propose a unified personalization framework that trains a personal model using adaptively weighted support users, including both similar and dissimilar individuals. The objective integrates personal loss, similarity-weighted transfer from similar users, and contrastive regularization from dissimilar users to suppress misleading correlations. An iterative optimization algorithm jointly updates model parameters and user similarity weights. Experiments on six tasks across four real-world digital health datasets show consistent improvements over population and personalized baselines. The method achieves up to 10% lower RMSE on large-scale datasets and approximately 25% lower RMSE in low-data settings. The learned adaptive weights improve data efficiency and provide interpretable guidance for targeted data selection.