A Blockchained Federated Learning Framework for Cognitive Computing in Industry 4.0 Networks

Qu, Youyang; Pokhrel, Shiva Raj; Garg, Sahil; Gao, Longxiang; Xiang, Yong

doi:10.1109/tii.2020.3007817

Cited by 248 publications

(102 citation statements)

References 25 publications

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“…FL provides the ability of distributed learning at local IoT devices by using both static data and data streams, while blockchain can provide high degrees of security with immutable ledgers and smart contracts that can provide authentication for learning interactions in FL implementation. Blockchain is also employed in [172] to provide security for FL implementation in industrial 4.0 networks. By using linked transactions, learning updates are verified and transmitted effectively for model aggregation, which in return accelerates learning convergence for FL algorithms.…”

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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“…The authors developed a double-masking protocol to guarantee the confidentiality of users' local gradients during the training phase. Qu et al in (238) developed a congitive model based on federated learning for industrial purposes. In order to guarantee the security of the process, they enabled a blockchain framework to secure the engine from poisoning attacks.…”

Section: Federated Learningmentioning

confidence: 99%

Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm

Kotsiopoulos

Sarigiannidis

Ioannidis

et al. 2021

Computer Science Review

193

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Industry 4.0 is the new industrial revolution. By connecting every machine and activity through network sensors to the Internet, a huge amount of data is generated. Machine Learning (ML) and Deep Learning (DL) are two subsets of Artificial Intelligence (AI), which are used to evaluate the generated data and produce valuable information about the manufacturing enterprise, while introducing in parallel the Industrial AI (IAI). In this paper, the principles of the Industry 4.0 are highlighted, by giving emphasis to the features, requirements, and challenges behind Industry 4.0. In addition, a new architecture for AIA is presented. Furthermore, the most important ML and DL algorithms used in Industry 4.0 are presented and compiled in detail. Each algorithm is discussed and evaluated in terms of its features, its applications, and its efficiency. Then, we focus on one of the most important Industry 4.0 fields, namely the smart grid, where ML and DL models are presented and analyzed in terms of efficiency and effectiveness in smart grid applications. Lastly, trends and challenges in the field of data analysis in the context of the new Industrial era are highlighted and discussed such as scalability, cybersecurity, and big data.

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“…A smart contract development framework is used to deploy and test smart contracts. Table 2 below shows a list of frameworks used to build smart contracts [20].…”

Section: Contract Layermentioning

confidence: 99%

Approaches towards Blockchain Innovation: A Survey and Future Directions

2021

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A blockchain is a decentralized peer to peer platform which provides security services based on some key concepts, namely authentication, confidentiality, integrity and authorization. It is the process of recording and keeping track of the resources without the intervention of a centralized authority. This paper provides an overview of blockchains, the structure of blockchains, consensus algorithms, etc., It compares the algorithms based on their utility and limitations. Though blockchains provide secure communication, there are some minimal data leaks which are discussed. Various security issues in blockchains are discussed such as denial of service attacks, etc., In addition to security, some other blockchain challenges are presented like scalability, reliability, interoperability, privacy and consensus mechanisms for integration with AI, IoT and edge computing. This paper also explains about the importance of blockchains in the fields of smart healthcare, smart grid, and smart financial systems. Overall, this paper gives the glimpse of various protocols, algorithms, applications, challenges and opportunities that are found in the blockchain domain.

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A Blockchained Federated Learning Framework for Cognitive Computing in Industry 4.0 Networks

Cited by 248 publications

References 25 publications

Federated Learning for Internet of Things: A Comprehensive Survey

Federated Learning for Internet of Things: A Comprehensive Survey

Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm

Approaches towards Blockchain Innovation: A Survey and Future Directions

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