# FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regions Source: [FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regions]() Collection index: [llms.txt]() Data updated: 2025-12-18T05:00:04.344Z Authors: Darpit Dave, Kathan Vyas, Jagadish Kumaran Jayagopal, Alfredo Garcia, Madhav Erraguntla, Mark Lawley Published: 2024-08-25T19:51:27.000Z ## Abstract Continuous glucose monitoring (CGM) devices provide real-time glucose monitoring and timely alerts for glycemic excursions, improving glycemic control among patients with diabetes. However, identifying rare events like hypoglycemia and hyperglycemia remain challenging due to their infrequency. Moreover, limited access to sensitive patient data hampers the development of robust machine learning models. Our objective is to accurately predict glycemic excursions while addressing data privacy concerns. To tackle excursion prediction, we propose a novel Hypo-Hyper (HH) loss function, which significantly improves performance in the glycemic excursion regions. The HH loss function demonstrates a 46% improvement over mean-squared error (MSE) loss across 125 patients. To address privacy concerns, we propose FedGlu, a machine learning model trained in a federated learning (FL) framework. FL allows collaborative learning without sharing sensitive data by training models locally and sharing only model parameters across other patients. FedGlu achieves a 35% superior glycemic excursion detection rate compared to local models. This improvement translates to enhanced performance in predicting both, hypoglycemia and hyperglycemia, for 105 out of 125 patients. These results underscore the effectiveness of the proposed HH loss function in augmenting the predictive capabilities of glucose predictions. Moreover, implementing models within a federated learning framework not only ensures better predictive capabilities but also safeguards sensitive data concurrently. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Glucose Forecasting on OhioT1DM PH = 60 minutes](): Glucose forecasting; OhioT1DM PH = 60 minutes; RMSE (PH=60min): 29.25. ## Original sources and implementation [Original paper]() [PDF]()