Towards Fairness-Aware Federated Learning-Reference-Cited by-同舟云学术

Towards Fairness-Aware Federated Learning

Published:2023 Issue: Volume: Page:1-17
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Shi Yuxin¹^ORCID,Yu Han²^ORCID,Leung Cyril³^ORCID

Affiliation:

1. School of Computer Science and Engineering and the Alibaba-NTU Singapore Joint Research Institute, Nanyang Technological University (NTU), Singapore, Singapore

2. School of Computer Science and Engineering, Nanyang Technological University (NTU), Singapore, Singapore

3. Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, Canada

Funder

National Research Foundation Singapore and the DSO National Laboratories through the AI Singapore Programme

Alibaba Group through the Alibaba Innovative Research (AIR) Program

Alibaba-NTU Singapore Joint Research Institute

Nanyang Technological University, Singapore

Nanyang Assistant Professorship

RIE 2020 Advanced Manufacturing and Engineering (AME) Programmatic Fund, Singapore

Future Communications Research & Development Programme

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Link

http://xplorestaging.ieee.org/ielx7/5962385/6104215/10097767.pdf?arnumber=10097767

Cited by 28 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

2. Genomic privacy preservation in genome-wide association studies: taxonomy, limitations, challenges, and vision;Briefings in Bioinformatics;2024-07-25

3. Reliable federated learning based on dual-reputation reverse auction mechanism in Internet of Things;Future Generation Computer Systems;2024-07

4. Hire When You Need to: Gradual Participant Recruitment for Auction-Based Federated Learning;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

5. Multi-Objective Distributed Client Selection in Federated Learning-Assisted Internet of Vehicles;Sensors;2024-06-27