Data Valuation in Federated Learning with Jian Pei

Data Valuation in Federated Learning with Jian Pei - YouTube

Abstract: To enable practical federated learning, we not only have to improve the efficiency but also address the incentive and fairness ...

Distinguished Professor Jian Pei Sheds Light on Data Valuation in ...

To address these issues, Professor Pei highlighted the importance of Data Valuation in Federated Learning. While the existing Federated Shapley ...

Improving Fairness for Data Valuation in Horizontal Federated ...

Federated Shapley value, recently proposed by Wang et al. [Federated Learning, 2020], is a measure for data value under the framework of federated learning that ...

Data Valuation in Federated Learning - Duke Computer Science

Particularly, valuation in federated learning seeks to allocate credits to participants in a just and equitable manner. Personalization in ...

Improving Fairness for Data Valuation in Horizontal Federated ...

Improving Fairness for Data Valuation in Horizontal. Federated Learning. Zhenan Fan∗, Huang Fang∗, Zirui Zhou†, Jian Pei‡, Michael P. Friedlander ...

Improving fairness for data valuation in federated learning

Fan, Z; Fang, H; Zhou, Z; Pei, J; Friedlander, MP; Liu, C; Zhang, Y. Published in: arXiv preprint arXiv:2109.09046. 2021. Duke Scholars. Jian Pei.

Keynote speaker - UDML 2024 - Philippe Fournier-Viger

jian pei. Prof. Jian PEI. Professor and Chair, Duke University ... Title: Data valuation in federated learning. Abstract. To enable ...

Improving Fairness for Data Valuation in Federated Learning

Federated Shapley value, recently proposed by Wang et al. [Federated Learning, 2020], is a measure for data value under the framework of ...

Department of Computer Science, HKBU's post - Facebook

[Distinguished Professor Jian Pei Sheds Light on Data Valuation in Federated Learning] Professor Jian Pei from Duke University recently delivered an ...

Keynotes

Particularly, valuation in federated learning seeks to allocate credits to participants in a just and equitable manner. Personalization in federated learning ...

Improving Fairness for Data Valuation in Federated Learning

Improving Fairness for Data Valuation in Federated Learning · Zhenan Fan, Huang Fang, +4 authors. Yong Zhang · Published in arXiv.org 2021 · Computer Science, ...

Fair and efficient contribution valuation for vertical federated learning

Abstract:Federated learning is a popular technology for training machine learning models on distributed data sources without sharing data.

Jian Pei | Huang Fang

Latest. Improving Fairness for Data Valuation in Federated Learning · Fair and efficient contribution valuation for vertical federated learning. Powered by ...

Jian Pei, Ph.D. - Publications - Google Sites

"Improving Fairness for Data Valuation in Horizontal Federated Learning". In Proceedings of the Thirty-eighth IEEE International Conference on Data ...

Jian Pei | Papers With Code

The success of federated learning depends largely on the participation of data owners. Data Valuation · Fairness +1.

Fair and efficient contribution valuation for vertical federated learning

99.90. Improving Fairness for Data Valuation in Horizontal Federated Learning. Zhenan Fan, Huang Fang, Zirui Zhou, Jian Pei, Michael P. Friedlander, Changxin ...

COMP Distinguished Lecture: Data Valuation in Federated Learning

SWT501 (Council Chamber), Shaw Tower, Shaw Campus · Professor Jian Pei.

Poster: Verifiable Data Valuation with Strong Fairness in Horizontal ...

Data valuation for each data provider becomes a critical issue to guarantee the fairness of federated learning by estimating the dataset quality ...

daviddao/awesome-data-valuation - GitHub

This paper focuses on fairness in data valuation within federated learning. The authors propose a new measure called completed federated Shapley value to ...

Improving Fairness for Data Valuation in Horizontal Federated ...

Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model.