【Author】
Qu, Youyang; Pokhrel, Shiva Raj; Garg, Sahil; Gao, Longxiang; Xiang, Yong
【Source】IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
【Abstract】Cognitive computing, a revolutionary AI concept emulating human brain's reasoning process, is progressively flourishing in the Industry 4.0 automation. With the advancement of various AI and machine learning technologies the evolution toward improved decision making as well as data-driven intelligent manufacturing has already been evident. However, several emerging issues, including the poisoning attacks, performance, and inadequate data resources, etc., have to be resolved. Recent research works studied the problem lightly, which often leads to unreliable performance, inefficiency, and privacy leakage. In this article, we developed a decentralized paradigm for big data-driven cognitive computing (D2C), using federated learning and blockchain jointly. Federated learning can solve the problem of data island with privacy protection and efficient processing while blockchain provides incentive mechanism, fully decentralized fashion, and robust against poisoning attacks. Using blockchain-enabled federated learning help quick convergence with advanced verifications and member selections. Extensive evaluation and assessment findings demonstrate D2C's effectiveness relative to existing leading designs and models.
【Keywords】Big data-driven; blockchain; cognitive computing; federated learning; Industry 4.0; smart manufacturing
【标题】工业 4.0 网络中用于认知计算的区块链联邦学习框架
【摘要】认知计算是一种模拟人脑推理过程的革命性人工智能概念，在工业 4.0 自动化中逐渐蓬勃发展。随着各种人工智能和机器学习技术的进步，向改进决策和数据驱动智能制造的演变已经很明显。然而，一些新出现的问题，包括中毒攻击、性能和数据资源不足等，都必须解决。最近的研究工作对该问题进行了简单的研究，这往往导致性能不可靠、效率低下和隐私泄露。在本文中，我们联合使用联邦学习和区块链，为大数据驱动的认知计算 (D2C) 开发了一种去中心化范式。联邦学习可以通过隐私保护和高效处理来解决数据孤岛问题，而区块链提供激励机制，完全去中心化的时尚，并且对中毒攻击具有鲁棒性。使用支持区块链的联邦学习有助于快速融合高级验证和成员选择。广泛的评估和评估结果证明了 D2C 相对于现有领先设计和模型的有效性。
【关键词】大数据驱动；区块链；认知计算；联邦学习；工业4.0；智能制造
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