Daniel Heinlein, Anton Akusok, Kaj-Mikael Björk, Leonardo Espinosa-Leal
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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.
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).