Block-chain based federated learning for knowledge capital

【Author】 Chen, Xi; Xiao, Bin; Xu, Qingzhen; He, Chengying; Lin, Jianwu

【Source】2020 INTERNATIONAL CONFERENCE ON IDENTIFICATION, INFORMATION AND KNOWLEDGE IN THE INTERNET OF THINGS (IIKI2020)

【Abstract】With the rapid development of deep learning and structures of neural networks, parameter settings of trained neural networks become new products from knowledge. Since supervised learning is used in 85% of machine learning to train the neural networks, well-labeled training data becomes new knowledge capital. In order to stimulate the knowledge sharing and protect the Intelligent Property (IP) right and privacy of knowledge capital at the same time, we need a framework to conduct training of neural networks with certain encrypted data and exchange training results with certain rewards. Federated learning provides a framework to train the networks without disclose the privacy and a block-chain based transaction system can organize a group of participants under certain consensus and reward mechanism. Their combination is an effective solution to manage knowledge capital, called knowledge capital bank in this paper. This paper defines the economic meaning of knowledge capital and all behaviors of related participants. Then we propose a block-chain based federated learning algorithm for secured knowledge sharing with IP and privacy protection. Finally, a complete design for a use case of this algorithm called Knowledge capital bank is demonstrated. (C) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the International Conference on Identification, Information and Knowledge in the internet of Things, 2020.

【Keywords】Federated Learning; Knowledge Capital Bank; Blockchain

【标题】基于区块链的知识资本联邦学习

【摘要】随着深度学习和神经网络结构的快速发展，经过训练的神经网络的参数设置成为知识的新产物。由于 85% 的机器学习使用监督学习来训练神经网络，因此标记良好的训练数据成为新的知识资本。为了激发知识共享，同时保护知识资本的知识产权（IP）权利和隐私，我们需要一个框架，用一定的加密数据对神经网络进行训练，并以一定的奖励交换训练结果。联邦学习提供了在不泄露隐私的情况下训练网络的框架，基于区块链的交易系统可以在一定的共识和奖励机制下组织一组参与者。它们的结合是管理知识资本的有效解决方案，本文称为知识资本银行。本文定义了知识资本的经济意义以及相关参与者的所有行为。然后，我们提出了一种基于区块链的联邦学习算法，用于通过 IP 和隐私保护进行安全知识共享。最后，演示了该算法用例的完整设计，称为知识资本银行。 (C) 2021 年作者。由 Elsevier B.V. 出版 这是一篇在 CC BY-NC-ND 许可下的开放获取文章 (https://creativecommons.org/licenses/by-nc-nd/4.0) 由国际科学委员会负责同行评审2020 年物联网识别、信息和知识会议。

【关键词】联邦学习；知识资本银行；区块链

【发表时间】2021

【收录时间】2022-07-06

【文献类型】Proceedings Paper

【论文大主题】区块链联邦学习

【论文小主题】联邦学习为主体

【翻译者】石东瑛