PFLF: Privacy-Preserving Federated Learning Framework for Edge Computing-Reference-Cited by-同舟云学术

PFLF: Privacy-Preserving Federated Learning Framework for Edge Computing

Author:

Zhou Hao¹^ORCID,Yang Geng¹^ORCID,Dai Hua¹^ORCID,Liu Guoxiu²

Affiliation:

1. School of Computer Science, Nanjing University of Post and Telecommunication, Nanjing, China

2. School of Network and Communication Engineering, Jinling Institute of Technology, Anhui, Wuhu, China

Funder

National Natural Science Foundation of China

Postgraduate Research and Practice Innovation Program of Jiangsu Province

Natural Research Foundation of Nanjing University of Posts and Telecommunications

Anhui Provincial Key Laboratory of Network and Information Security

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Safety, Risk, Reliability and Quality

Link

Reference46 articles.

1. Local privacy and statistical minimax rates;duchi;Proc Annu IEEE Symp Foundations Comput Sci,2013

3. A generic framework for privacy preserving deep learning;ryffel;arXiv 1811 04017,2018

4. Learning differentially private recurrent language models;mcmahan;arXiv 1710 06963,2017

5. Differentially private meta-learning;li;arXiv 1909 05830,2019

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