GluKDnet: A Lightweight Blood Glucose Prediction Model Based on Heterogeneous Knowledge Distillation

Accurate blood glucose prediction is essential for glycemic management in people with diabetes, but the size of many high-performing models complicates execution on resource-constrained artificial pancreas controllers. We propose GluKDnet, a lightweight glucose-forecasting model for prospective Android-smartphone-based mobile edge controllers. GluKDnet transfers the representational capacity of a time-series foundation model to a compact causal CNN through heterogeneous knowledge distillation. The teacher model, MOMENT, is adapted to continuous glucose monitoring (CGM) data through risk-event-aware masking, which prioritizes abnormal glucose levels, rapid glucose fluctuations, and CGM-defined dawn phenomenon and Somogyi effect patterns during masked reconstruction. A transient-state and steady-state distillation module jointly aligns ordered patch-level dynamics and day-level summaries between teacher and student. Using DLCP3 for teacher pretraining and leave-one-patient-out evaluation on OhioT1DM, GluKDnet achieves RMSE values of 20.04, 32.04, and 45.33 mg/dL for 30, 60, and 120 min prediction, respectively, with about 53K parameters. Auxiliary evaluation on T1D-UoM shows a similar offline accuracy–parameter count pattern. On a vivo V2072A Android smartphone, the 30 min model achieved a mean API inference latency of 0.470 ms (P95: 0.855 ms), a maximum sampled process proportional-set-size memory of 47.06 MiB, and a median incremental device energy estimate of 0.277 mJ per inference. These device measurements characterize the exported student model under one hardware and software configuration; insulin dosing and prospective closed-loop clinical evaluation remain outside the scope of this study.