Secure decentralized peer-to-peer training of deep neural networks based on distributed ledger technology

Fadaeddini, Amin; Majidi, Babak; Eshghi, Mohammad

doi:10.1007/s11227-020-03251-9

Cited by 17 publications

(11 citation statements)

References 14 publications

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“…Similar work is done in the latest research by Fadaeddini et al [36], who proposed a framework where the privacy of data-owners was preserved by training the shared model on their data locally. After the learning is completed, the data-owners only shared the learned parameters of the model.…”

Section: Security Effortsmentioning

confidence: 80%

“…Furthermore, the same data can be manipulated and raise doubts on its integrity. To improve upon the above architecture, several studies have been proposed [36,90,92,93,115]. Mendis et al [91] proposed fully autonomous individual contributors working in a decentralized fashion without disturbing the functionality and overall efficiency, which they later on improved in their work in [92].…”

Section: Security Effortsmentioning

confidence: 99%

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Security and Privacy in IoT Using Machine Learning and Blockchain

et al. 2020

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Security and privacy of users have become significant concerns due to the involvement of the Internet of Things (IoT) devices in numerous applications. Cyber threats are growing at an explosive pace making the existing security and privacy measures inadequate. Hence, everyone on the Internet is a product for hackers. Consequently, Machine Learning (ML) algorithms are used to produce accurate outputs from large complex databases, where the generated outputs can be used to predict and detect vulnerabilities in IoT-based systems. Furthermore, Blockchain (BC) techniques are becoming popular in modern IoT applications to solve security and privacy issues. Several studies have been conducted on either ML algorithms or BC techniques. However, these studies target either security or privacy issues using ML algorithms or BC techniques, thus posing a need for a combined survey on efforts made in recent years addressing both security and privacy issues using ML algorithms and BC techniques. In this article, we provide a summary of research efforts made in the past few years, from 2008 to 2019, addressing security and privacy issues using ML algorithms and BC techniques in the IoT domain. First, we discuss and categorize various security and privacy threats reported in the past 12 years in the IoT domain. We then classify the literature on security and privacy efforts based on ML algorithms and BC techniques in the IoT domain. Finally, we identify and illuminate several challenges and future research directions using ML algorithms and BC techniques to address security and privacy issues in the IoT domain.

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Section: Security Effortsmentioning

confidence: 80%

Section: Security Effortsmentioning

confidence: 99%

Security and Privacy in IoT Using Machine Learning and Blockchain

et al. 2020

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“…Moreover, the authors introduced a proof-of-storage scheme for rewarding users that provide storage for the deep learning models. Fadaeddini et al [ 18 ] proposed a secure decentralized peer-to-peer framework in order to train deep neural network models on distributed ledger technology on Stellar blockchain [ 27 ]. A Deep Learning Coin (DLC) is proposed for blockchain compensation.…”

Section: Related Workmentioning

confidence: 99%

Privacy-preserving federated learning for scalable and high data quality computational-intelligence-as-a-service in Society 5.0

Peyvandi

Majidi

Peyvandi

et al. 2022

Multimed Tools Appl

Self Cite

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Training supervised machine learning models like deep learning requires high-quality labelled datasets that contain enough samples from various categories and specific cases. The Data as a Service (DaaS) can provide this high-quality data for training efficient machine learning models. However, the issue of privacy can minimize the participation of the data owners in DaaS provision. In this paper, a blockchain-based decentralized federated learning framework for secure, scalable, and privacy-preserving computational intelligence, called Decentralized Computational Intelligence as a Service (DCIaaS), is proposed. The proposed framework is able to improve data quality, computational intelligence quality, data equality, and computational intelligence equality for complex machine learning tasks. The proposed framework uses the blockchain network for secure decentralized transfer and sharing of data and machine learning models on the cloud. As a case study for multimedia applications, the performance of DCIaaS framework for biomedical image classification and hazardous litter management is analysed. Experimental results show an increase in the accuracy of the models trained using the proposed framework compared to decentralized training. The proposed framework addresses the issue of privacy-preserving in DaaS using the distributed ledger technology and acts as a platform for crowdsourcing the training process of machine learning models.

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“…•Cloud computing security attacks [154] •Protect privacy in Blockchain through federated learning [155] •Try to build a deep learning model using Blockchain [156] •Fusion of Blockchain and distributed learning [157] Cloud technology…”

Section: E Future Research Directionsmentioning

confidence: 99%

Bibliometric Analysis of the Blockchain Scientific Evolution: 2014–2020

Luo

Bai

2021

IEEE Access

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As one of the disruptive technologies in the fourth industrial revolution, Blockchain has irrevocably changed how people live and work. Many scholars have studied the Blockchain, and the number of related publications has increased over time. Bibliometrics is an excellent method to study the development trend of a specific field. However, few scholars have used bibliometrics to research Blockchain; the inheritance and evolution among its different research themes have not yet been fully understood. This study emphasizes the scientific evolution and research focus of Blockchain through bibliometric analysis. We analyzed 2993 publications from 2014 to 2020 using co-word analysis and cluster analysis with bibliometric tools such as the SciMAT. The most productive author, country, institutions and leading research areas were identified, outlining the Blockchain field's fundamental developments. The results show the changes in keywords regarding Blockchain summarize the research hotspots in different periods. The study discusses the evolution process of various themes in each period, providing a meaningful reference for scholars to identify research hotspots and future research directions in the Blockchain field.

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Secure decentralized peer-to-peer training of deep neural networks based on distributed ledger technology

Cited by 17 publications

References 14 publications

Security and Privacy in IoT Using Machine Learning and Blockchain

Security and Privacy in IoT Using Machine Learning and Blockchain

Privacy-preserving federated learning for scalable and high data quality computational-intelligence-as-a-service in Society 5.0

Bibliometric Analysis of the Blockchain Scientific Evolution: 2014–2020

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