Learning Cautiously in Federated Learning with Noisy and Heterogeneous Clients-Reference-Cited by-同舟云学术

Learning Cautiously in Federated Learning with Noisy and Heterogeneous Clients

Published:2023-07 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Multimedia and Expo (ICME)
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Author:

Wu Chenrui¹,Li Zexi²,Wang Fangxin³,Wu Chao⁴

Affiliation:

1. The Chinese University of Hong Kong,The Future Network of Intelligence Institute,Shenzhen

2. Zhejiang University,College of Computer Science and Technology

3. The Chinese University of Hong Kong,School of Science and Engineering,Shenzhen

4. Zhejiang University,School of Public Affairs

Funder

National Natural Science Foundation of China

Research and Development

Zhejiang University

Fundamental Research Funds for the Central Universities

Publisher

IEEE

Link

Reference21 articles.

1. Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling;zhang;Advances in neural information processing systems,2021

2. Focus: Dealing with label quality disparity in federated learning;chen,2020

3. Learning with bad training data via iterative trimmed loss minimization;shen;Proceedings of International Conference on Machine Learning,2019

4. mixup: Beyond empirical risk minimization;zhang;Proceedings of ICLR,2018

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