Realizing Health 4.0 in Beyond 5G Networks

Abstract

The advancements of Edge and Internet of Things (IoT) devices in terms of their processing, storage and communication capabilities, in addition to the advancements in wireless communication and networking technologies, have led to the rise in Intelligent Edge-enabled IoT architectures. Federated Learning (FL) is one example in which intelligence is adapted to the edge to offload some of the processing load from centralized entities and maintain secure localized model training. With Health 4.0, it is anticipated that distributed and edge-supported Artificial Intelligence (AI) will enable faster and more accurate early-stage disease discovery that relies significantly on intelligent remote and on-site IoT devices. Given that healthcare systems are highly scrutinized by both governments and patients to maintain high levels of data privacy and security, FL coupled with the support of blockchain will provide an optimal solution to reinforce today's healthcare frameworks. In this paper, we propose a FL-enabled framework for healthcare systems that is supported by edge-computing, blockchain and intelligent IoT devices. The solution considers a pneumonia detection use-case as a proof-of-concept and is applicable to an extended set of health-related use-cases. Different pre-trained models are compared against the proposed FL-supported model, namely, CNN, GG16, VGG19, InceptionV3, ResNet, DenseNet, and Xception. Results show high model accuracy attainment and significant improvements in terms of data privacy.