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Personalized Digital Health Modeling with Adaptive Support Users

arXiv 2605.02004 ↗First posted 3 May 2026cs.AIcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 38 references that are in the map. First posted on arXiv 3 May 2026.

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References · 23

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2020Supervised Contrastive Learningcited by 2,803
  3. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  4. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  5. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  6. 2020Adaptive Personalized Federated Learningcited by 353
  7. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 309
  8. 2017Multitask learning and benchmarking with clinical time series datacited by 287
  9. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 282
  10. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  11. 2021FedBABU: Towards Enhanced Representation for Federated Image Classificationcited by 180
  12. 2018Federated Meta-Learning with Fast Convergence and Efficient Communicationcited by 154
11 more
  1. 2022GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalizationcited by 28
  2. 2024ChatDiet: Empowering Personalized Nutrition-Oriented Food Recommender Chatbots through an LLM-Augmented Frameworkcited by 20
  3. 2023GPFedRec: Graph-guided Personalization for Federated Recommendationcited by 18
  4. 2021Connecting Low-Loss Subspace for Personalized Federated Learningcited by 12
  5. 2021Personalized Stress Monitoring using Wearable Sensors in Everyday Settingscited by 10
  6. 2022Auxo: Efficient Federated Learning via Scalable Client Clusteringcited by 8
  7. 2024Integrating Wearable Sensor Data and Self-reported Diaries for Personalized Affect Forecastingcited by 7
  8. 2023Personalized Federated Learning with Attention-based Client Selectioncited by 5
  9. 2024The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learningcited by 3
  10. 2024Loneliness Forecasting Using Multi-modal Wearable and Mobile Sensing in Everyday Settingscited by 3
  11. 2022Objective Prediction of Tomorrow's Affect Using Multi-Modal Physiological Data and Personal Chronicles: A Study of Monitoring College Student Well-being in 2020cited by 1

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