释放区块链联邦学习融合在物联网安全方面的前景:全面回顾

IF 13.3 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Mansi Gupta , Mohit Kumar , Renu Dhir
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引用次数: 0

摘要

物联网(IoT)是一个革命性的典范,它为人类生活带来了自动化和便捷性,并改善了人类的生活体验。智能家居、医疗保健和农业就是其中一些令人惊叹的应用案例。这些物联网应用通常采用机器学习(ML)技术来增强其功能。ML 可用于分析各种传感器数据,包括优化智能家居的能源使用、预测工业设备的维护需求、可穿戴设备的个性化用户体验以及检测安全监控的异常情况。然而,实施集中式 ML 技术并不可行,因为计算能力成本高昂,而且大量数据存储在云服务器上,存在隐私问题。为了保护数据隐私,Federated Learning(FL)成为集中式 ML 方法的新典范,FL 是一种 ML 变体,它将模型发送到用户设备,而无需向第三方或中央服务器提供隐私数据,是解决数据泄漏问题的有前途的解决方案之一。FL 将原始数据保存在客户端,只向中央服务器传送模型更新或参数,有助于减少隐私泄露。不过,它仍然无法抵御攻击。区块链提供了一种解决方案,利用智能合约和共识机制保护支持 FL 的物联网网络。本手稿回顾了物联网的应用和挑战,讨论了可用于在确保隐私的同时训练物联网网络的 FL 技术,并对现有工作进行了分析。为了确保物联网应用的安全性和隐私性,介绍了一个基于区块链驱动的FL综合框架,并利用这三种强大的范式对现有研究进行了研究。最后,探讨了集成平台所面临的研究挑战,以及物联网与其他尖端技术结合的潜在应用前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unleashing the prospective of blockchain-federated learning fusion for IoT security: A comprehensive review
Internet-of-things (IoT) is a revolutionary paragon that brings automation and easiness to human lives and improves their experience. Smart Homes, Healthcare, and Agriculture are some of their amazing use cases. These IoT applications often employ Machine Learning (ML) techniques to strengthen their functionality. ML can be used to analyze sensor data for various, including optimizing energy usage in smart homes, predicting maintenance needs in industrial equipment, personalized user experiences in wearable devices, and detecting anomalies for security monitoring. However, implementing centralized ML techniques is not viable because of the high cost of computing power and privacy issues since so much data is stored over a cloud server. To safeguard data privacy, Federated Learning (FL) has become a new paragon for centralized ML methods where FL,an ML variation sends a model to the user devices without the need to give private data to the third-party or central server, it is one of the promising solutions to address data leakage concerns. By saving raw data to the client itself and transferring only model updates or parameters to the central server, FL helps to reduce privacy leakage. However, it is still not attack-resistant. Blockchain offers a solution to protect FL-enabled IoT networks using smart contracts and consensus mechanisms. This manuscript reviews IoT applications and challenges, discusses FL techniques that can be used to train IoT networks while ensuring privacy, and analyzes existing work. To ensure the security and privacy of IoT applications, an integrated Blockchain-powered FL-based framework was introduced and studies existing research were done using these three powerful paradigms. Finally, the research challenges faced by the integrated platform are explored for future scope, along with the potential applications of IoT in conjunction with other cutting-edge technologies.
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来源期刊
Computer Science Review
Computer Science Review Computer Science-General Computer Science
CiteScore
32.70
自引率
0.00%
发文量
26
审稿时长
51 days
期刊介绍: Computer Science Review, a publication dedicated to research surveys and expository overviews of open problems in computer science, targets a broad audience within the field seeking comprehensive insights into the latest developments. The journal welcomes articles from various fields as long as their content impacts the advancement of computer science. In particular, articles that review the application of well-known Computer Science methods to other areas are in scope only if these articles advance the fundamental understanding of those methods.
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