  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.
