Integration of blockchain and federated learning for Internet of Things: Recent advances and future challenges

【Author】 Ali, Mansoor; Karimipour, Hadis; Tariq, Muhammad

【Source】COMPUTERS & SECURITY

【Abstract】The role of the Internet of Things (IoT) in the revolutionized society cannot be overlooked. The IoT can leverage advanced machine learning (ML) algorithms for its applications. However, given the fact of massive data, which is stored at a central cloud server, adopting centralized machine learning algorithms is not a viable option due to immense computation cost and privacy leakage issues. Given such conditions, blockchain can be leveraged to enhance the privacy of IoT networks by making them decentralized without any central authority. Nevertheless, the sensitive and massive data that is stored in distributive fashion, leveraged it for application purpose, is still a challenging task. To overcome this challenging task, federated learning (FL), which is a new breed of ML is the most promising solution that brings learning to the end devices without sharing the private data to the central server. In the FL mechanism, the central server act as an orchestrator to start the FL learning process, and only model parameters' updates are shared between end devices and the central orchestrator. Although FL can provide better privacy and data management, it is still in the development phase and has not been adopted by various communities due to its unknown privacy issues. In this paper first, we present the notion of blockchain and its application in IoT systems. Then we describe the privacy issues related to the implementation of blockchain in IoT and present privacy preservation techniques to cope with the privacy issues. Second, we introduce the FL application in IoT systems, devise a taxonomy, and present privacy threats in FL. Afterward, we present IoT-based use cases on envisioned dispersed federated learning and introduce blockchain-based traceability functions to improve privacy. Finally, open research gaps are addressed for future work. (c) 2021 Elsevier Ltd. All rights reserved.

【Keywords】Federated learning; The Internet of Things; BLockchains; Privacy; Dispersed federated learning

【标题】物联网区块链和联邦学习的整合：最新进展和未来挑战

【摘要】物联网 (IoT) 在变革社会中的作用不容忽视。物联网可以在其应用程序中利用先进的机器学习 (ML) 算法。然而，鉴于大量数据存储在中央云服务器上，由于巨大的计算成本和隐私泄露问题，采用集中式机器学习算法并不是一个可行的选择。在这种情况下，可以利用区块链在没有任何中央权威的情况下使物联网网络去中心化，从而增强物联网网络的隐私。尽管如此，以分布式方式存储的敏感和海量数据，并将其用于应用目的，仍然是一项具有挑战性的任务。为了克服这一具有挑战性的任务，作为一种新型 ML 的联邦学习 (FL) 是最有前途的解决方案，它可以将学习带到终端设备，而无需将私有数据共享到中央服务器。在 FL 机制中，中央服务器充当协调器来启动 FL 学习过程，并且只有模型参数的更新在终端设备和中央协调器之间共享。虽然 FL 可以提供更好的隐私和数据管理，但它仍处于开发阶段，由于其未知的隐私问题尚未被各个社区采用。在本文中，我们首先介绍了区块链的概念及其在物联网系统中的应用。然后我们描述了与在物联网中实施区块链相关的隐私问题，并提出了解决隐私问题的隐私保护技术。其次，我们介绍了物联网系统中的 FL 应用，设计了一种分类法，并提出了 FL 中的隐私威胁。之后，我们提出了基于物联网的设想分散联邦学习用例，并引入了基于区块链的可追溯性功能以改善隐私。最后，为未来的工作解决了开放的研究空白。 (c) 2021 Elsevier Ltd. 保留所有权利。

【关键词】联邦学习；物联网；区块链；隐私;分散的联邦学习

【发表时间】2021

【收录时间】2022-07-06

【文献类型】Article

【论文大主题】区块链联邦学习

【论文小主题】联邦学习为主体

【影响因子】5.105

【翻译者】石东瑛