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Real-Time Prediction Algorithm for intelligent Edge networks with Federated Learning-Based Modeling

Computers, Materials & Continua 2023年第11期77卷 1967-1983页

作者： Seungwoo Kang Seyha Ros Inseok Song Prohim Tam Sa Math Seokhoon Kim Department of Software Convergence Soonchunhyang UniversityAsan31538Korea Department of Telecommunication and Electronic Engineering Royal University of Phnom PenhPhnom Penh12156Cambodia Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Korea

intelligent healthcare networks represent a significant component in digital applications,where the requirements hold within quality-of-service(QoS)reliability and safeguarding *** paper addresses these requirements through the integration of enabler paradigms,including federated learning(FL),cloud/edge computing,softwaredefined/virtualized networking infrastructure,and converged prediction *** study focuses on achieving reliability and efficiency in real-time prediction models,which depend on the interaction flows and network *** response to these challenges,we introduce a modified version of federated logistic regression(FLR)that takes into account convergence latencies and the accuracy of the final FL model within healthcare *** establish the FLR framework for mission-critical healthcare applications,we provide a comprehensive workflow in this paper,introducing framework setup,iterative round communications,and model evaluation/*** optimization process delves into the formulation of loss functions and gradients within the domain of federated optimization,which concludes with the generation of service experience batches for model *** assess the practicality of our approach,we conducted experiments using a hypertension prediction model with data sourced from the 2019 annual dataset(Version 2.0.1)of the Korea Medical Panel *** metrics,including end-to-end execution delays,model drop/delivery ratios,and final model accuracies,are captured and compared between the proposed FLR framework and other baseline *** study offers an FLR framework setup for the enhancement of real-time prediction modeling within intelligent healthcare networks,addressing the critical demands of QoS reliability and privacy preservation.

关键词： Edge computing federated logistic regression intelligent healthcare networks prediction modeling privacy-aware and real-time learning

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