VFL: A Verifiable Federated Learning With Privacy-Preserving for Big Data in Industrial IoT

Fu, Anmin; Zhang, Xianglong; Xiong, Naixue; Gao, Yansong; Wang, Huaqun; Zhang, Jing

doi:10.1109/tii.2020.3036166

Cited by 153 publications

(78 citation statements)

References 26 publications

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“…In this way, FL helps users (i.e., smart home owners) to build home assistant solutions such as object detection and control their home by the coordination of distributed IoT devices in a privacy-preserving manner. Another project in [183] focuses on a verifiable FL platform to achieve efficient and secure model training in industrial IoT. A verification mechanism is built at the FL clients (i.e., industrial IoT devices) to verify the accuracy of the aggregated results based on the characteristics of Lagrange interpolation, which allows devices to detect forged results in the FL training.…”

Section: Model M2mentioning

confidence: 99%

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen

Ding

Pathirana

et al. 2021

IEEE Commun. Surv. Tutorials

684

182

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Section: Model M2mentioning

confidence: 99%

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen

Ding

Pathirana

et al. 2021

IEEE Commun. Surv. Tutorials

684

182

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“…There is also the problem of data leakage. The study [21] presents the danger of a malicious Federated Learning server that sends forged weights to participants, then analyze the plaintext weights that are sent back to expose their data. The authors then propose a weight encryption scheme that help clients individually find out whether the weights they get from the server are legitimate or not.…”

Section: Related Workmentioning

confidence: 99%

LocKedge: Low-Complexity Cyberattack Detection in IoT Edge Computing

et al. 2021

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“…For instance, research shows that parameter sharing with the server is sufficient for an attacker to infer knowledge of underlying data. Moreover, since the models are being trained locally, an adversary can attack local models on a client, eventually affecting the global model at the server [111]. Therefore, several efforts are also made to address these issues.…”

Section: Federated Learning and Iiotmentioning

confidence: 99%

“…The authors claim that using their approach, a client can share the data in cipher state with the server and the server can train the model using data in cipher state. Similarly, the authors of [111] provide a secure gradient aggregation framework. The authors in [113], [114] provide a comprehensive survey on the security and privacy of FL.…”

Section: Federated Learning and Iiotmentioning

confidence: 99%

Edge Intelligence for Data Handling and Predictive Maintenance in IIOT

Hafeez

McArdle

2021

IEEE Access

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The use of IoT has become pervasive and IoT devices are common in many domains. Industrial IoT (IIoT) utilises IoT devices and sensors to monitor machines and environments to ensure optimal performance of equipment and processes. Predictive Maintenance (PM) which monitors the health of machines to determine the probable failure of components is one IIoT technique which is receiving attention lately. To achieve effective PM, massive amounts of data are collected, processed and ultimately analysed by Machine Learning (ML) algorithms. Traditionally IoT sensors transmit their data readings to the cloud for processing and modelling. Handling and transmitting massive amounts of data between IoT devices and infrastructure has a cost. Edge Computing (EC) in which both sensors and intermediate nodes can process data provides opportunities to reduce data transmission costs and increase processing speed. This article examines IIoT for PM and discusses how and where data can be processed and analysed. Initially, this article presents sampling and data reduction techniques. These techniques allow for a reduction in the amount of data transmitted to the cloud for processing but there are potential accuracy trade-offs when ML algorithms utilise reduced datasets. An alternative approach is to move ML algorithms closer to the data to reduce data transmission. There are three main techniques that utilise the EC paradigm to perform ML and data processing on intermediary nodes. These techniques are categorized according to where data processing occurs: Device and Edge, Edge and Cloud and Device and Cloud (Federated Learning). In addition to exploring traditional approaches, these three state-of-the-art techniques are examined in this article and their benefits and weaknesses are presented. A novel architecture to demonstrate how EC can be utilized both for data reduction and PM in IIoT is also proposed.

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VFL: A Verifiable Federated Learning With Privacy-Preserving for Big Data in Industrial IoT

Cited by 153 publications

References 26 publications

Federated Learning for Internet of Things: A Comprehensive Survey

Federated Learning for Internet of Things: A Comprehensive Survey

LocKedge: Low-Complexity Cyberattack Detection in IoT Edge Computing

Edge Intelligence for Data Handling and Predictive Maintenance in IIOT

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