Dynamic heterogeneous federated learning with multi-level prototypes-Reference-Cited by-同舟云学术

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Dynamic heterogeneous federated learning with multi-level prototypes

Published:2024-09 Issue: Volume:153 Page:110542
ISSN:0031-3203
Container-title:Pattern Recognition
language:en
Short-container-title:Pattern Recognition

Author:

Guo Shunxin^ORCID,Wang Hongsong^ORCID,Geng Xin^ORCID

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference45 articles.

1. Communication-efficient learning of deep networks from decentralized data;McMahan,2017

2. Reliable and privacy-preserving federated learning with anomalous users;ZHANG;ZTE Communications,2023

3. Doubly contrastive representation learning for federated image recognition;Zhang;Pattern Recognit.,2023

4. Fast deep autoencoder for federated learning;Novoa-Paradela;Pattern Recognit.,2023

5. Federated learning-outcome prediction with multi-layer privacy protection;Zhang;Frontiers of Computer Science,2024

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