Secure deep learning for distributed data against maliciouscentral server.
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 p...
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Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2022-01-01
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Series: | PLoS ONE |
Online Access: | https://doi.org/10.1371/journal.pone.0272423 |
Summary: | 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. |
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ISSN: | 1932-6203 |