Secure deep learning for distributed data against malicious central server

Le Trieu Phong

PLOS ONE, 2022, vol. 17, issue 8, 1-15

Abstract: In this paper, we propose a secure system for performing deep learning with distributed trainers connected to a central parameter server. Our system has the following two distinct features: (1) the distributed trainers can detect malicious activities in the server; (2) the distributed trainers can perform both vertical and horizontal neural network training. In the experiments, we apply our system to medical data including magnetic resonance and X-ray images and obtain approximate or even better area-under-the-curve scores when compared to the existing scores.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0272423

DOI: 10.1371/journal.pone.0272423

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