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Private neural network auctions for additive manufacturing
Shukla, Amey
Shukla, Amey
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2023-05
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Thesis
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Electronic dissertations
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Abstract
Additive Manufacturing is changing the way we construct, deliver, and consume objects by
allowing customers to quickly build custom objects on-demand and in locations near to them.
Additive manufacturing operators need to optimally control the manufacturing process, monitor
tasks in real-time and set the prices for their built objects. Moreover, operators need to protect
private data related to the prices paid by customers and their purchased objects. Existing
works focus on either optimal control, real-time monitoring, price setting, or privacy, missing
the advantages of jointly addressing them. Existing works also require vast computational
resources to accomplish only one of these tasks. To address these issues, we develop a
differentially-private distributed neural network auction that optimally allocates manufacturing
resources and sets prices in a way that maximizes the profit of the manufacturer. The auction
protects the privacy of the customers’ bids. Moreover, to reduce the computing time, we design
a parallel computation algorithm for the neural network that is executed by a cluster of edge
computing devices. We evaluate the proposed neural network through extensive simulations.
We observe that it can jointly perform the operators’ tasks while maintaining the privacy of the
customers. Our simulations also show that the parallel algorithm reduces the execution time.
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Thesis (M.S.)-- Wichita State University, College of Engineering, School of Computing
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Wichita State University
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© Copyright 2023 by Amey Shukla
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