增强型DASS-CARE 2.0:基于区块链的去中心化FL框架

IF 1.8 4区 计算机科学 Q3 TELECOMMUNICATIONS
Meryeme Ayache, Ikram El Asri, Jamal N. Al-Karaki, Mohamed Bellouch, Amjad Gawanmeh, Karim Tazzi
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引用次数: 1

摘要

在2019冠状病毒病大流行期间,认知医疗物联网(CIoMT)的出现具有变革性。CIoMT是一项快速发展的技术,它利用人工智能、大数据和物联网(IoT)来提供个性化的患者护理。CIoMT可用于监测和跟踪生命体征,如体温、血压和心率,从而为医疗保健提供者提供有关患者健康状况的实时信息。然而,在这样的系统中,数据处理或共享过程中的隐私问题仍然存在。因此,联邦学习(FL)通过允许多个医疗设备以分布式和隐私保护的方式安全地协作,在认知医疗物联网(CIoMT)中发挥着重要作用。另一方面,经典的集中式FL模型有一些局限性,例如单点故障和恶意服务器。本文通过使用基于区块链的联邦学习框架,对现有的das - care 2.0框架进行了增强。该解决方案为多组织环境下的医疗数据共享和分析提供了一个安全可靠的分布式学习平台。基于区块链的联邦学习框架为克服传统FL中遇到的挑战提供了一种创新的解决方案。此外,我们在考虑上述安全挑战的同时,通过对我们的DASS-CARE 2.0和传统集中式FL模型的比较研究,全面讨论了所提出框架的优势。总体而言,与传统方法相比,所提出的框架的性能显示出显着的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Enhanced DASS-CARE 2.0: a blockchain-based and decentralized FL framework

Enhanced DASS-CARE 2.0: a blockchain-based and decentralized FL framework

The emergence of the Cognitive Internet of Medical Things (CIoMT) during the COVID-19 pandemic has been transformational. The CIoMT is a rapidly evolving technology that uses artificial intelligence, big data, and the Internet of Things (IoT) to provide personalized patient care. The CIoMT can be used to monitor and track vital signs, such as temperature, blood pressure, and heart rate, thus giving healthcare providers real-time information about a patient’s health. However, in such systems, the problem of privacy during data processing or sharing remains. Therefore, federated learning (FL) plays an important role in the Cognitive Internet of Medical Things (CIoMT) by allowing multiple medical devices to securely collaborate in a distributed and privacy-preserving manner. On the other hand, classical centralized FL models have several limitations, such as single points of failure and malicious servers. This paper presents an enhancement of the existing DASS-CARE 2.0 framework by using a blockchain-based federated learning framework. The proposed solution provides a secure and reliable distributed learning platform for medical data sharing and analytics in a multi-organizational environment. The blockchain-based federated learning framework offrs an innovative solution to overcome the challenges encountered in traditional FL. Furthermore, we provide a comprehensive discussion of the advantages of the proposed framework through a comparative study between our DASS-CARE 2.0 and the traditional centralized FL model while taking the aforementioned security challenges into consideration. Overall, the performance of the proposed framework shows significant advantages compared to traditional methods.

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来源期刊
Annals of Telecommunications
Annals of Telecommunications 工程技术-电信学
CiteScore
5.20
自引率
5.30%
发文量
37
审稿时长
4.5 months
期刊介绍: Annals of Telecommunications is an international journal publishing original peer-reviewed papers in the field of telecommunications. It covers all the essential branches of modern telecommunications, ranging from digital communications to communication networks and the internet, to software, protocols and services, uses and economics. This large spectrum of topics accounts for the rapid convergence through telecommunications of the underlying technologies in computers, communications, content management towards the emergence of the information and knowledge society. As a consequence, the Journal provides a medium for exchanging research results and technological achievements accomplished by the European and international scientific community from academia and industry.
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