Dynamic Activation of Clients and Parameters for Federated Learning over Heterogeneous Graphs-Reference-Cited by-同舟云学术

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Dynamic Activation of Clients and Parameters for Federated Learning over Heterogeneous Graphs

Published:2023-04 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 39th International Conference on Data Engineering (ICDE)
language:
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Author:

Gu Zishan¹,Zhang Ke²,Bai Guangji³,Chen Liang⁴,Zhao Liang³,Yang Carl³

Affiliation:

1. Columbia University,Department of Computer Science,New York,United States

2. University of Hong Kong,Department of Computer Science,Hong Kong,China

3. Emory University,Department of Computer Science,Atlanta,United States

4. Sun Yat-sen University,Department of Computer Science,Guangzhou,China

Funder

Emory University

Nanjing University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10184508/10184509/10184557.pdf?arnumber=10184557

Reference48 articles.

1. Federated graph classification over non-iid graphs;xie;Thirty-Fifth Conference on Neural Information Processing Systems,2021

2. Curriculum learning

3. Subgraph federated learning with missing neighbor generation;zhang;Proceedings of the Conference on Neural Information Processing Systems (NeurIPS),2021

4. Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels;jiang;Proceedings of the International Conference on Machine Learning (ICML),2018

5. Fedgl: Federated graph learning framework with global self-supervision;chen,2021

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