【Author】
Miglani, Arzoo; Kumar, Neeraj
【Source】COMPUTER COMMUNICATIONS
【Abstract】Keeping in view of the constraints and challenges with respect to big data analytics along with security and privacy preservation for 5G and B5G applications, the integration of machine learning and blockchain, two of the most promising technologies of the modern era is inevitable. In comparison to the traditional centralized techniques for security and privacy preservation, blockchain uses decentralized consensus algorithms for verification and validation of different transactions which are supposed to become an integral part of blockchain network. Starting with the existing literature survey, we introduce the basic concepts of blockchain and machine learning in this article. Then, we presented a comprehensive taxonomy for integration of blockchain and machine learning in an IoT environment. We also explored federated learning, reinforcement learning, deep learning algorithms usage in blockchain based applications. Finally, we provide recommendations for future use cases of these emerging technologies in 5G and B5G technologies.
【Keywords】Blockchain; Machine learning; Federated learning; Internet of Things; Deep learning; 5G; 6G
【标题】5G 及以后网络中物联网环境的区块链管理和机器学习适应：系统评价
【摘要】鉴于大数据分析以及 5G 和 B5G 应用的安全性和隐私保护方面的限制和挑战，机器学习和区块链这两种现代最有前途的技术的集成是不可避免的。与传统的集中式安全和隐私保护技术相比，区块链使用分散的共识算法来验证和确认不同的交易，这些交易被认为是区块链网络不可或缺的一部分。从现有的文献调查开始，我们在本文中介绍了区块链和机器学习的基本概念。然后，我们提出了在物联网环境中集成区块链和机器学习的综合分类法。我们还探讨了联邦学习、强化学习、深度学习算法在基于区块链的应用程序中的使用。最后，我们为这些新兴技术在 5G 和 B5G 技术中的未来用例提供了建议。
【关键词】区块链；机器学习；联邦学习；物联网;深度学习； 5G； 6G
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