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用于深度学习的区块链：综述与开放挑战

Blockchain for deep learning: review and open challenges.

作者信息

Shafay Muhammad, Ahmad Raja Wasim, Salah Khaled, Yaqoob Ibrar, Jayaraman Raja, Omar Mohammed

机构信息

Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, 127788 UAE.

College of Engineering and Information Technology, Ajman University, Ajman, UAE.

出版信息

Cluster Comput. 2023;26(1):197-221. doi: 10.1007/s10586-022-03582-7. Epub 2022 Mar 14.

DOI:10.1007/s10586-022-03582-7
PMID:35309043
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8919362/
Abstract

Deep learning has gained huge traction in recent years because of its potential to make informed decisions. A large portion of today's deep learning systems are based on centralized servers and fall short in providing operational transparency, traceability, reliability, security, and trusted data provenance features. Also, training deep learning models by utilizing centralized data is vulnerable to the single point of failure problem. In this paper, we explore the importance of integrating blockchain technology with deep learning. We review the existing literature focused on the integration of blockchain with deep learning. We classify and categorize the literature by devising a thematic taxonomy based on seven parameters; namely, blockchain type, deep learning models, deep learning specific consensus protocols, application area, services, data types, and deployment goals. We provide insightful discussions on the state-of-the-art blockchain-based deep learning frameworks by highlighting their strengths and weaknesses. Furthermore, we compare the existing blockchain-based deep learning frameworks based on four parameters such as blockchain type, consensus protocol, deep learning method, and dataset. Finally, we present important research challenges which need to be addressed to develop highly trustworthy deep learning frameworks.

摘要

近年来，深度学习因其具有做出明智决策的潜力而获得了巨大的关注。当今的大部分深度学习系统基于集中式服务器，在提供操作透明度、可追溯性、可靠性、安全性和可信数据来源功能方面存在不足。此外，利用集中式数据训练深度学习模型容易受到单点故障问题的影响。在本文中，我们探讨了将区块链技术与深度学习相结合的重要性。我们回顾了专注于区块链与深度学习集成的现有文献。我们通过基于七个参数设计一个主题分类法对文献进行分类；即区块链类型、深度学习模型、深度学习特定共识协议、应用领域、服务、数据类型和部署目标。我们通过突出其优点和缺点，对基于区块链的最新深度学习框架进行了有见地的讨论。此外，我们基于区块链类型、共识协议、深度学习方法和数据集等四个参数比较了现有的基于区块链的深度学习框架。最后，我们提出了开发高度可信的深度学习框架需要解决的重要研究挑战。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9e7/8919362/c9180922c5be/10586_2022_3582_Fig9_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9e7/8919362/3de6e3457e52/10586_2022_3582_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9e7/8919362/c9180922c5be/10586_2022_3582_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9e7/8919362/81f0ea652f73/10586_2022_3582_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9e7/8919362/b8a31e360c75/10586_2022_3582_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9e7/8919362/3ff9de98b699/10586_2022_3582_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9e7/8919362/692d8c34dc10/10586_2022_3582_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9e7/8919362/852101e09b3e/10586_2022_3582_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9e7/8919362/91155e3b8290/10586_2022_3582_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9e7/8919362/02d02287861f/10586_2022_3582_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9e7/8919362/3de6e3457e52/10586_2022_3582_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9e7/8919362/c9180922c5be/10586_2022_3582_Fig9_HTML.jpg

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