Toward Quality-aware Data Valuation in Learning Algorithms: Practices, Challenges, and Beyond-Reference-Cited by-同舟云学术

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Toward Quality-aware Data Valuation in Learning Algorithms: Practices, Challenges, and Beyond

Published:2023 Issue: Volume: Page:1-1
ISSN:0890-8044
Container-title:IEEE Network
language:
Short-container-title:IEEE Network

Author:

Xu Lei¹^ORCID,Chen Jiaqing²,Chang Shan³^ORCID,Wang Cong⁴^ORCID,Li Bo⁵^ORCID

Affiliation:

1. School of Mathematics and Statistics, Nanjing University of Science and Technology, Nanjing, China

2. Department of Computer Science, Columbia University, New York, U.S.

3. School of Computer Science and Technology, Donghua University, Shanghai, China

4. Department of Computer Science, City University of Hong Kong, Hong Kong, China

5. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China

Funder

HK RGC

Research Grants Council of Hong Kong

Natural Science Foundation of Jiangsu Province

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Hardware and Architecture,Information Systems,Software

Link

http://xplorestaging.ieee.org/ielx7/65/7593428/10348525.pdf?arnumber=10348525

Cited by 1 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. FairFed: Improving Fairness and Efficiency of Contribution Evaluation in Federated Learning via Cooperative Shapley Value;IEEE INFOCOM 2024 - IEEE Conference on Computer Communications;2024-05-20

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