A Secure Distributed Learning Framework Using Homomorphic Encryption
Author:
Affiliation:
1. California State University, Sacramento,Department of Computer Science,Sacramento,CA,USA
2. Grand Valley State University,School of Computing,Allendale,MI,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10320127/10319983/10320176.pdf?arnumber=10320176
Reference34 articles.
1. BatchCrypt: Efficient homomorphic encryption for cross-silo federated learning;zhang;Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC 2020),2020
2. Differentially private federated learning: A client level perspective;geyer,2017
3. Remark on the security of CKKS scheme in practice;cheon;Cryptology ePrint Archive,2020
4. Personalized Federated Learning With Differential Privacy
5. DNN-DP: Differential Privacy Enabled Deep Neural Network Learning Framework for Sensitive Crowdsourcing Data
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