Newly diagnosed Type 1 Diabetes (T1D) patients often struggle to obtain effective Blood Glucose (BG) prediction models due to the lack of sufficient BG data from Continuous Glucose Monitoring (CGM), presenting a significant "cold start" problem in patient care. Utilizing population models to address this challenge is a potential solution, but collecting patient data for training population models in a privacy-conscious manner is challenging, especially given that such data is often stored on personal devices. Considering the privacy protection and addressing the "cold start" problem in diabetes care, we propose "GluADFL", blood Glucose prediction by Asynchronous Decentralized Federated Learning. We compared GluADFL with eight baseline methods using four distinct T1D datasets, comprising 298 participants, which demonstrated its superior performance in accurately predicting BG levels for cross-patient analysis. Furthermore, patients' data might be stored and shared across various communication networks in GluADFL, ranging from highly interconnected (e.g., random, performs the best among others) to more structured topologies (e.g., cluster and ring), suitable for various social networks. The asynchronous training framework supports flexible participation. By adjusting the ratios of inactive participants, we found it remains stable if less than 70% are inactive. Our results confirm that GluADFL offers a practical, privacy-preserving solution for BG prediction in T1D, significantly enhancing the quality of diabetes management.

翻译：新诊断的1型糖尿病患者常因缺乏连续血糖监测设备积累的足够血糖数据而难以获得有效的血糖预测模型，这在患者护理中构成了显著的"冷启动"问题。利用群体模型应对这一挑战是一种潜在解决方案，但以隐私保护的方式收集患者数据训练群体模型面临困难，尤其是考虑到此类数据通常存储在个人设备中。兼顾隐私保护与糖尿病护理中的"冷启动"问题，我们提出了"GluADFL"——基于异步去中心化联邦学习的血糖预测方法。我们使用包含298名参与者的四个独立1型糖尿病数据集，将GluADFL与八种基线方法进行比较，结果证明其在跨患者分析的血糖水平精准预测方面具有优越性能。此外，GluADFL中患者数据可在不同通信网络拓扑结构中存储与共享，包括高度互联拓扑（如随机拓扑——在比较中表现最佳）和更具结构化的拓扑（如集群与环状拓扑），适用于各类社交网络场景。异步训练框架支持灵活参与机制，通过调整非活跃参与者比例，我们发现当非活跃比例低于70%时系统仍保持稳定。研究结果证实，GluADFL为1型糖尿病血糖预测提供了实用且隐私保护的解决方案，显著提升了糖尿病管理质量。