# FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regions

arXiv: https://arxiv.org/abs/2408.13926 · HTML: https://mlsanity.com/paper/2408.13926/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 119 references that are in the map.
- First posted on arXiv 25 Aug 2024.

## Builds on (most cited first, 25 of 25)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare](https://mlsanity.com/paper/1907.09173/) (2019)
- [Federated Evaluation of On-device Personalization](https://mlsanity.com/paper/1910.10252/) (2019)
- [Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges](https://mlsanity.com/paper/1908.06847/) (2019)
- [Federated Learning: Opportunities and Challenges](https://mlsanity.com/paper/2101.05428/) (2021)
- [FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring](https://mlsanity.com/paper/2012.07450/) (2020)
- [Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data](https://mlsanity.com/paper/1810.08553/) (2018)
- [A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework](https://mlsanity.com/paper/2204.03719/) (2022)
- [A Federated Learning Framework for Healthcare IoT devices](https://mlsanity.com/paper/2005.05083/) (2020)
- [Convolutional Recurrent Neural Networks for Glucose Prediction](https://mlsanity.com/paper/1807.03043/) (2018)
- [Two-stage Federated Phenotyping and Patient Representation Learning](https://mlsanity.com/paper/1908.05596/) (2019)
- [Federated pretraining and fine tuning of BERT using clinical notes from multiple silos](https://mlsanity.com/paper/2002.08562/) (2020)
- [Robust Aggregation for Adaptive Privacy Preserving Federated Learning in Healthcare](https://mlsanity.com/paper/2009.08294/) (2020)
- [FedStack: Personalized activity monitoring using stacked federated learning](https://mlsanity.com/paper/2209.13080/) (2022)
- [Adversarial Multi-Source Transfer Learning in Healthcare: Application to Glucose Prediction for Diabetic People](https://mlsanity.com/paper/2006.15940/) (2020)
- [Learning Hidden Patterns from Patient Multivariate Time Series Data Using Convolutional Neural Networks: A Case Study of Healthcare Cost Prediction](https://mlsanity.com/paper/2009.06783/) (2020)
- [Forecasting with Sparse but Informative Variables: A Case Study in Predicting Blood Glucose](https://mlsanity.com/paper/2304.08593/) (2023)
- [Blockchain and AI-based Solutions to Combat Coronavirus (COVID-19)-like Epidemics: A Survey](https://mlsanity.com/paper/2106.14631/) (2021)
- [GLYFE: Review and Benchmark of Personalized Glucose Predictive Models in Type-1 Diabetes](https://mlsanity.com/paper/2006.15946/) (2020)
- [Privacy Preserved Blood Glucose Level Cross-Prediction: An Asynchronous Decentralized Federated Learning Approach](https://mlsanity.com/paper/2406.15346/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Proof-of-Concept Simulation-Driven Digital Twin Framework for Decision-Aware Diabetes Modeling](https://mlsanity.com/paper/2605.11247/) (2026)

## Abstract (arXiv)

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.