Towards a Holistic Approach to Chronic Disease Management: Integrating Federated Learning and IoT for Personalized health Care

Abstract

Chronic diseases, specifically Cardiovascular Disease (CVD), pose a substantial worldwide health obstacle, requiring innovative and comprehensive approaches to management. This study presents an innovative method for managing chronic diseases by combining Federated Learning (FL) and the Internet of Things (IoT). The goal is to offer tailored healthcare solutions. The study presents a new approach called Federated Transfer Learning (FTL) that incorporates Adaptive Gradient Clipping (AGC) to improve the performance of models and maintain privacy in a distributed network of healthcare devices.
 The primary objective of this study is to develop a holistic framework that seamlessly amalgamates data from various sources, including wearables, medical devices, and electronic health records, utilizing FL to build a centralized model while preserving data privacy at the individual level. The proposed Faster Than Light (FTL) algorithm with Automatic Gain Control (AGC) enhances the process of model convergence, guaranteeing stability and dependability in a federated learning environment. The research methodology entails implementing the suggested framework in the management of Cardiovascular Disease (CVD). The performance of the model is assessed using the Area Under the Curve (AUC) metric, resulting in an impressive AUC score of 92.4%. This highlights the efficacy of the integrated approach in precisely forecasting and controlling risks associated with cardiovascular diseases (CVD), showcasing its potential for widespread use in managing chronic diseases. The key findings emphasize the advantages of integrating IoT devices into the FL ecosystem, enabling the immediate surveillance and gathering of various health data. The decentralized nature of the learning process enables model training to occur without compromising data privacy, making it suitable for large-scale healthcare systems. Moreover, the implementation of Faster Than Light (FTL) with Automatic Gain Control (AGC) improves the resilience and effectiveness of the federated learning procedure, thereby enhancing the overall achievement of the suggested framework. In conclusion, this study presents a groundbreaking contribution to the field of chronic disease management, specifically targeting Cardiovascular Disease. The combination of Federated Learning and IoT, along with the innovative Federated Transfer Learning with Adaptive Gradient Clipping, not only achieves outstanding predictive accuracy but also guarantees the privacy and security of sensitive health data. The proposed framework holds significant promise for revolutionizing personalized healthcare solutions, paving the way for a more effective and patient-centric approach to chronic disease management.