Privacy-Preserving Weighted Federated Learning Within the Secret Sharing Framework

Privacy-Preserving Weighted Federated Learning Within the Secret Sharing Framework

This paper studies privacy-preserving weighted federated learning within the secret sharing framework, where individual private data is split into random shares which are distributed among a set of pre-defined computing servers. The contribution of this paper mainly comprises the following four-fold...

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Bibliographic Details
Main Authors: Huafei Zhu, Rick Siow Mong Goh, Wee-Keong Ng
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Beaver-triple
El Gamal encryption
privacy-preserving
secure multi-party computation
secret share
weighted federated learning
Online Access:https://ieeexplore.ieee.org/document/9244122/

Internet

https://ieeexplore.ieee.org/document/9244122/

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