Federated hospital: a multilevel federated learning architecture for dealing with heterogeneous data distribution in the context of smart hospitals services - Policarpo, Lucas Micol (Universidade do Vale do Rio dos Sinos, 2023)
The integration of artificial intelligence (AI) and machine learning (ML) services in healthcare has revolutionized patient care, ranging from real-time health monitoring to complex medical image analysis. However, deploying these ML services in the context of smart hospitals poses significant challenges due to varying data demands and privacy concerns. Federated Learning (FL) emerges as a promising solution by allowing data to remain with users while training ML models collaboratively. FL ensures data privacy and offers scalability by enabling distributed learning across multiple users. In this research, we extend the FL paradigm to the domain of smart hospitals and propose the "Federated Hospital" model to address the challenges posed by heterogeneity among diferente hospital departments. By leveraging multi-level aggregation, the Federated Hospital architecture is designed to accommodate the diverse demands and health situations within individual departments, providing personalized and accurate ML models for each user. Through extensive experimentation and evaluation in distinct scenarios, including homogeneous and heterogeneous data distributions, we compare the performance of the Federated Hospital model against standard ML and FL approaches. The results confirm the effectiveness of our proposal in terms of accuracy, efficiency, and convergence speed. Moreover, the multi-level aggregation process in the smart hospital architecture enhances model performance, ensuring the generation of tailored ML models specific to each department’s unique characteristics. The Federated Hospital model demonstrates its potential to improve the execution of MLoriented services in smart hospitals. By optimizing the accuracy and performance of ML models for diverse healthcare departments, our proposal aims to revolutionize data-driven decisionmaking, promoting personalized patient care and efficient healthcare services. The next step of this research is to execute Federated Hospital in real hospitals in the metropolitan area of Porto Alegre, Rio Grande do Sul.
Keywords: Aprendizado federado; hospitais inteligentes; Distribuição de dados desbalanceada; Federated learning; Smart hospital; Unbalance data distribution; ACCNPQ::Ciências Exatas e da Terra::Ciência da Computação