Author:
Liu Fengxia,Zheng Zhiming,Shi Yexuan,Tong Yongxin,Zhang Yi
Abstract
AbstractFederated learning is a promising learning paradigm that allows collaborative training of models across multiple data owners without sharing their raw datasets. To enhance privacy in federated learning, multi-party computation can be leveraged for secure communication and computation during model training. This survey provides a comprehensive review on how to integrate mainstream multi-party computation techniques into diverse federated learning setups for guaranteed privacy, as well as the corresponding optimization techniques to improve model accuracy and training efficiency. We also pinpoint future directions to deploy federated learning to a wider range of applications.
Publisher
Springer Science and Business Media LLC
Subject
General Computer Science,Theoretical Computer Science
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