Secure Model Fusion for Distributed Learning Using Partial Homomorphic Encryption

Pages: 154 - 179
Published: Jan 1, 2019
Abstract
Distributed learning has emerged as a useful tool for analyzing data stored in multiple geographic locations, especially when the distributed data sets are large and hard to move around, or the data owner is reluctant to put data into the Cloud due to privacy concerns. In distributed learning, only the locally computed models are uploaded to the fusion server, which however may still cause privacy issues since the fusion server could implement...
Paper Details
Title
Secure Model Fusion for Distributed Learning Using Partial Homomorphic Encryption
Published Date
Jan 1, 2019
Pages
154 - 179
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