FDFL: Fair and Discrepancy-Aware Incentive Mechanism for Federated Learning-Reference-Cited by-同舟云学术

FDFL: Fair and Discrepancy-Aware Incentive Mechanism for Federated Learning

Published:2024 Issue: Volume:19 Page:8140-8154
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Chen Zhe¹^ORCID,Zhang Haiyan¹^ORCID,Li Xinghua¹^ORCID,Miao Yinbin¹^ORCID,Zhang Xiaohan²^ORCID,Zhang Man¹^ORCID,Ma Siqi³^ORCID,Deng Robert H.⁴^ORCID

Affiliation:

1. State Key Laboratory of Integrated Services Networks, the School of Cyber Engineering, and the Engineering Research Center of Big Data Security, Ministry of Education, Xidian University, Xi’an, China

2. Institute of Cyber Science and Technology, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

3. School of Systems and Computing, The University of New South Wales, Sydney, NSW, Australia

4. School of Information Systems, Singapore Management University, Bras Basah, Singapore

Funder

National Natural Science Foundation of China

“111 Center”

Key Research and Development Projects of Shaanxi Province

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/10206/10319981/10609774.pdf?arnumber=10609774

Reference39 articles.

1. Communication-efficient learning of deep networks from decentralized data;McMahan

2. Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach

3. Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

4. DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-based Incentive

5. A Robust Game-Theoretical Federated Learning Framework With Joint Differential Privacy