Industrial Internet Federated Learning Driven by IoT Equipment ID and Blockchain

Zhang, Xu; Hou, Haibo; Zhao, Fang; Wang, Zhiqian

doi:10.1155/2021/7705843

Cited by 6 publications

(8 citation statements)

References 25 publications

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“…Most federated learning with Blockchain integrations were with the iIoT infrastructure. Works of [44][45][46] primarily focused on applying privacy measures, designing an auditable record of transactions, and eliminating the central aggregation server of federated learning in their Blockchain integration. In [45], the authors designed a system that detects iIoT device failure.…”

Section: Other Solutionsmentioning

confidence: 99%

Federated Learning and Blockchain Integration for Privacy Protection in the Internet of Things: Challenges and Solutions

Asqah

Moulahi

2023

Future Internet

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The Internet of Things (IoT) compromises multiple devices connected via a network to perform numerous activities. The large amounts of raw user data handled by IoT operations have driven researchers and developers to provide guards against any malicious threats. Blockchain is a technology that can give connected nodes means of security, transparency, and distribution. IoT devices could guarantee data centralization and availability with shared ledger technology. Federated learning (FL) is a new type of decentralized machine learning (DML) where clients collaborate to train a model and share it privately with an aggregator node. The integration of Blockchain and FL enabled researchers to apply numerous techniques to hide the shared training parameters and protect their privacy. This study explores the application of this integration in different IoT environments, collectively referred to as the Internet of X (IoX). In this paper, we present a state-of-the-art review of federated learning and Blockchain and how they have been used in collaboration in the IoT ecosystem. We also review the existing security and privacy challenges that face the integration of federated learning and Blockchain in the distributed IoT environment. Furthermore, we discuss existing solutions for security and privacy by categorizing them based on the nature of the privacy-preservation mechanism. We believe that our paper will serve as a key reference for researchers interested in improving solutions based on mixing Blockchain and federated learning in the IoT environment while preserving privacy.

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Section: Other Solutionsmentioning

confidence: 99%

Federated Learning and Blockchain Integration for Privacy Protection in the Internet of Things: Challenges and Solutions

Asqah

Moulahi

2023

Future Internet

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“…Its applicability is claimed to be in a variety of fields such as autonomous vehicles, traffic prediction, and monitoring, healthcare, telecom, IoT, pharmaceutics, industrial management [74], industrial IoT [75], and healthcare and medical AI [76]. The proportion of the trend to used FL in different fields is depicted in Figure 7.…”

Section: Applications Of Federated Learningmentioning

confidence: 99%

Applications of Federated Learning; Taxonomy, Challenges, and Research Trends

et al. 2022

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The federated learning technique (FL) supports the collaborative training of machine learning and deep learning models for edge network optimization. Although a complex edge network with heterogeneous devices having different constraints can affect its performance, this leads to a problem in this area. Therefore, some research can be seen to design new frameworks and approaches to improve federated learning processes. The purpose of this study is to provide an overview of the FL technique and its applicability in different domains. The key focus of the paper is to produce a systematic literature review of recent research studies that clearly describes the adoption of FL in edge networks. The search procedure was performed from April 2020 to May 2021 with a total initial number of papers being 7546 published in the duration of 2016 to 2020. The systematic literature synthesizes and compares the algorithms, models, and frameworks of federated learning. Additionally, we have presented the scope of FL applications in different industries and domains. It has been revealed after careful investigation of studies that 25% of the studies used FL in IoT and edge-based applications and 30% of studies implement the FL concept in the health industry, 10% for NLP, 10% for autonomous vehicles, 10% for mobile services, 10% for recommender systems, and 5% for FinTech. A taxonomy is also proposed on implementing FL for edge networks in different domains. Moreover, another novelty of this paper is that datasets used for the implementation of FL are discussed in detail to provide the researchers an overview of the distributed datasets, which can be used for employing FL techniques. Lastly, this study discusses the current challenges of implementing the FL technique. We have found that the areas of medical AI, IoT, edge systems, and the autonomous industry can adapt the FL in many of its sub-domains; however, the challenges these domains can encounter are statistical heterogeneity, system heterogeneity, data imbalance, resource allocation, and privacy.

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“…Zhang et al [18] propose an Industrial Internet identification using blockchain and federated learning technologies, which provides privacy protection. Liu et al [19] develop an on-device FL-based deep anomaly detection system for IIoT time series data sensing, which detects edge devices' failure in IIoT industrial product production.…”

Section: Applications Of Federated Learningmentioning

confidence: 99%

Federated Learning and its Applications for Security and Communication

Asif¹,

Karim²,

Kausar³

2022

IJACSA

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The not so long ago, Artificial Intelligence (AI) has revolutionized our life by giving rise to the idea of self-learning in different environments. Amongst its different variants, Federated Learning (FL) is a novel approach that relies on decentralized communication data and its associated training. While reducing the amount of data acquired from users, federated learning derives the benefits of popular machine learning techniques, it brings learning to the edge or directly on-device. FL, frequently referred to as a new dawn in AI, is still in its early stages and is yet to garner widespread acceptance, owing to its (unknown) security and privacy implications. In this paper, we give an illustrative explanation of FL techniques, communication, and applications with privacy as well as security issues. According to our findings, there are fewer privacy-specific dangers linked with FL than security threats. We conclude the paper with the challenges of FL with special emphases on security.

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Industrial Internet Federated Learning Driven by IoT Equipment ID and Blockchain

Cited by 6 publications

References 25 publications

Federated Learning and Blockchain Integration for Privacy Protection in the Internet of Things: Challenges and Solutions

Federated Learning and Blockchain Integration for Privacy Protection in the Internet of Things: Challenges and Solutions

Applications of Federated Learning; Taxonomy, Challenges, and Research Trends

Federated Learning and its Applications for Security and Communication

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