PrivacyEAFL: Privacy-Enhanced Aggregation for Federated Learning in Mobile Crowdsensing-Reference-Cited by-同舟云学术

PrivacyEAFL: Privacy-Enhanced Aggregation for Federated Learning in Mobile Crowdsensing

Published:2023 Issue: Volume:18 Page:5804-5816
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Zhang Mingwu¹^ORCID,Chen Shijin¹,Shen Jian²^ORCID,Susilo Willy³^ORCID

Affiliation:

1. School of Computers, Hubei University of Technology, Wuhan, China

2. School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, China

3. School of Computing and Information Technology, Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, NSW, Australia

Funder

National Natural Science Foundation of China

Key Research and Development Program of Hubei Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

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

Link

http://xplorestaging.ieee.org/ielx7/10206/9970396/10251646.pdf?arnumber=10251646

Reference46 articles.

1. FederatedTree: A Secure Serverless Algorithm for Federated Learning to Reduce Data Leakage

2. Practical Privacy-Preserving Federated Learning in Vehicular Fog Computing

3. CrowdFL: Privacy-Preserving Mobile Crowdsensing System via Federated Learning

4. SSBAS-FA: A secure sealed-bid e-auction scheme with fair arbitration based on time-released blockchain

5. FedSky: An Efficient and Privacy-preserving Scheme for Federated Mobile Crowdsensing

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