私有线性方程求解:在联邦学习和极限学习机中的应用

IF 3.7 3区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Daniel Heinlein, Anton Akusok, Kaj-Mikael Björk, Leonardo Espinosa-Leal
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引用次数: 0

摘要

在联合学习中,多个设备使用自己的私有数据计算通用机器学习模型的一部分。然后在构建聚合模型的中央服务器中交换这些部分模型(或它们的参数)。这个共享过程可能会泄露用于训练他们的数据信息。随着机器学习模型变得更简单,这个问题也会加剧,这表明单隐藏层前馈神经网络(如极限学习机)的风险更高。在本文中,我们建立了一种机制,将输入数据伪装成线性方程系统,同时保证修改不会改变解,并提出了两种可能的方法将这些技术应用于联邦学习。我们的研究结果表明,极限学习机可以用于具有额外安全层的联邦学习,使它们在计算资源有限的学习方案中具有吸引力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Private linear equation solving: An application to federated learning and extreme learning machines
In federated learning, multiple devices compute each a part of a common machine learning model using their own private data. These partial models (or their parameters) are then exchanged in a central server that builds an aggregated model. This sharing process may leak information about the data used to train them. This problem intensifies as the machine learning model becomes simpler, indicating a higher risk for single-hidden-layer feedforward neural networks, such as extreme learning machines. In this paper, we establish a mechanism to disguise the input data to a system of linear equations while guaranteeing that the modifications do not alter the solutions, and propose two possible approaches to apply these techniques to federated learning. Our findings show that extreme learning machines can be used in federated learning with an extra security layer, making them attractive in learning schemes with limited computational resources.
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来源期刊
Journal of Computational Science
Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
5.50
自引率
3.00%
发文量
227
审稿时长
41 days
期刊介绍: Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).
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