A personalized hybrid federated learning framework for glucose prediction in type 1 diabetes

Deep learning methods for glucose prediction in type 1 diabetes (T1D) are often constrained by data availability, restricted access to data, and substantial heterogeneity across treatment settings and institutions. Federated Learning offers a privacy-preserving alternative to centralized training, but its performance under clinically realistic, non-independent and non-identically distributed (non-IID) data remains challenging. This work proposes a hybrid federated-personalized framework that combines cross-silo training with patient-level personalization for multi-horizon glucose prediction. Clinically defined silos based on treatment modality and insulin type are used to explicitly model real-world heterogeneity. A Temporal Fusion Transformer is trained using federated optimization (FedProx), incorporating clinician-prescribed basal insulin profiles as known future inputs. The resulting global model is subsequently adapted at the patient level, enabling personalization. The proposed approach is evaluated on the T1-DEXI dataset and externally validated on the OhioT1DM cohort. Centralized and federated personalization are compared to quantify performance differences between in-distribution and external evaluation and to assess patient-specific adaptation. Results show that the federated global models reduce prediction errors at longer horizons compared to centralized training on unseen data distributions, whereas centralized training achieves lower in-distribution errors. After patient-level personalization, centralized models show slightly lower absolute errors than federated models. These findings are consistent with a balance between population-level behavior and individual adaptation under the evaluated configurations. They support the feasibility of combining cross-silo training with patient-level personalization for privacy-preserving glucose prediction in heterogeneous clinical environments. Furthermore, incorporating scheduled basal insulin profiles enhances in-distribution prediction accuracy, underscoring the importance of clinically meaningful inputs in model design.