AccScience Publishing / AIH / Online First / DOI: 10.36922/AIH026050010
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REVIEW ARTICLE

Reinforcement and federated learning for privacy-preserving glucose regulation: A review

Sherif Abdelfattah1* ,  Rahul Biswa Karma1 ,  Rohit Biswa Karma1 ,  Babu Baniya1
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1 Department of Computer Science and Information Systems, Bradley University, Peoria, IL, United States of America
AIH, 026050010 https://doi.org/10.36922/AIH026050010
Received: 31 January 2026 | Revised: 9 March 2026 | Accepted: 12 March 2026 | Published online: 24 April 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Type 1 diabetes mellitus (T1DM) requires continuous monitoring of blood glucose levels and careful insulin dosing to maintain safe glycemic control. Reinforcement learning (RL) has emerged as a promising approach for automated glucose regulation in T1DM by enabling adaptive insulin dosing through sequential decision-making. However, real-world deployment remains constrained by safety requirements, inter-patient variability, limited clinical data, and strict privacy regulations that hinder centralized model development. This review aims to synthesize recent advances in RL-based automated insulin delivery (AID) and examine how federated learning (FL) can enable privacy-preserving personalization across distributed clinical and device data. We organize the literature into key themes, including: (i) online RL controllers developed primarily in simulation environments, (ii) offline RL methods that learn dosing policies from logged glucose-insulin trajectories (e.g., batch-constrained and conservative value-learning approaches), (iii) safety-aware strategies aimed at minimizing hypoglycemia risk and improving robustness under practical disturbances, and (iv) hybrid architectures integrating glucose prediction and control. We further discuss FL designs for personalization in heterogeneous, non-identically distributed patient data and highlight their relevance to diabetes management. Across the studies reviewed, most reinforcement learning approaches for glucose control are evaluated in simulation environments, while only a limited number use real-world clinical or continuous glucose monitoring (CGM) datasets. Federated learning studies are emerging for privacy-preserving personalization, and only a small subset of works currently explore hybrid RL–FL or federated reinforcement learning frameworks. Finally, we identify open challenges related to sim-to-real generalization, clinical validation, interpretability, scalability, and privacy risks in distributed learning, and outline future directions for robust, clinically viable next-generation diabetes management systems.

Graphical abstract
Keywords
Reinforcement learning
Offline reinforcement learning
Federated learning
Privacy-preserving healthcare AI
Artificial pancreas
Diabetes management
Safe control
Funding
This work was supported in part through Caterpillar Fellowship from Bradley University
Conflict of interest
The authors declare they have no competing interests.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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