Nodes selection review for federated learning in the blockchain‐based internet of things

IF 1.5 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Security and Privacy Pub Date : 2023-09-28 DOI:10.1002/spy2.344
Mohammed Riyadh Abdmeziem, Hiba Akli, Rima Zourane
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Abstract

Abstract Internet of Things (IoT) gained momentum these last few years pushed by the emergence of fast and reliable communication networks such as 5G and beyond. IoT depends on collecting information from the environment, leading to a significant increase in the amount of data generated that needs to be transmitted, saved, and analyzed. It is clear that classical deterministic approaches might not be suitable to this complex and fast evolving environment. Hence, machine learning techniques with their ability to handle such a dynamic context, are rising in popularity. In particular, Federated Learning architectures which are better suited to the distributed nature of IoT and its privacy concerns. Besides, to address security risks such as model poisoning, device compromise, and network interception, Blockchain (BC) is seen as the secure and distributed underlying communication infrastructure of choice. This integration of IoT, FL, and BC remains in its early stages and several challenges arise. Indeed, nodes selection to perform resource intensive and critical operations like model learning and transactions validation is a crucial issue considering the strong heterogeneity of the involved devices in terms of resources. In this paper, we propose an original literature review including a taxonomy, a thorough analysis, a comparison of the proposed approaches, along with some open research directions.
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基于区块链的物联网中联合学习的节点选择审查
在5G等快速可靠的通信网络出现的推动下,物联网(IoT)在过去几年中获得了发展势头。物联网依赖于从环境中收集信息,这导致需要传输、保存和分析的生成数据量显着增加。很明显,经典的确定性方法可能不适合这种复杂和快速发展的环境。因此,机器学习技术及其处理这种动态上下文的能力越来越受欢迎。特别是联邦学习架构,它更适合物联网的分布式特性及其隐私问题。此外,为了解决模型中毒、设备泄露和网络拦截等安全风险,区块链(BC)被视为安全、分布式的底层通信基础设施的选择。物联网、FL和BC的整合仍处于早期阶段,并出现了一些挑战。实际上,考虑到所涉及的设备在资源方面具有很强的异质性,选择节点执行资源密集型和关键操作(如模型学习和事务验证)是一个关键问题。在本文中,我们提出了一个原始文献综述,包括分类,深入分析,比较所提出的方法,以及一些开放的研究方向。
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