首页|A survey on federated learning:a perspective from multi-party computation
A survey on federated learning:a perspective from multi-party computation
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Federated 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.
federated learningmulti-party computationprivacy-preserving data miningdistributed learning
Institute of Artificial Intelligence and Key Laboratory of Mathematics Informatics Behavioral Semantics,Beihang University,Beijing 100191,China
State Key Laboratory of Software Development Environment and Advanced Innovation Center for Future Blockchain and Privacy Computing,Beihang University,Beijing 100191,China
Pengcheng Laboratory,Shenzhen 518055,China
Zhongguancun Laboratory,Beijing 100190,China
Institute for Mathematical Sciences and Engineering Research Center of Financial Computing and Digital Engineering,Renmin University of China,Beijing 100872,China
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National Natural Science Foundation of China(NSFC)National Natural Science Foundation of China(NSFC)National Natural Science Foundation of China(NSFC)Funding of Advanced Innovation Center for Future Blockchain and Privacy ComputingBeihang University Basic Research FundingWeBank Scholars Program
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