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Fed-UserPro: A user profile construction method based on federated learning

Yilin Fan

Yilin Fan

Information Technology School, Hebei University of Economics and BusinessChina
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Fan, Yilin
, 
Zheng Huo

Zheng Huo

Information Technology School, Hebei University of Economics and BusinessChina
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Huo, Zheng
 und 
Yaxin Huang

Yaxin Huang

Information Technology School, Hebei University of Economics and BusinessChina
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Sciendo|Google Scholar
Huang, Yaxin
  
20. Mai 2022
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Online veröffentlicht: 20. Mai 2022
Seitenbereich: 2301 - 2314
Eingereicht: 14. Feb. 2022
Akzeptiert: 10. Apr. 2022
DOI: https://doi.org/10.2478/amns.2021.2.00188
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© 2023 Yilin Fan et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Federated learning architecture
Federated learning architecture

Fig. 2

LDA graphical model representation. LDA, latent Dirichlet allocation
LDA graphical model representation. LDA, latent Dirichlet allocation

Fig. 3

Experiment of changing the local epoch size of participants. (a) Accuracy (b) Macro-average
Experiment of changing the local epoch size of participants. (a) Accuracy (b) Macro-average

Fig. 4

Experiment of changing the batch size of the participants. (a) Accuracy (b) Macro-average
Experiment of changing the batch size of the participants. (a) Accuracy (b) Macro-average

Fig. 5

Experiments to change the number of participants. (a) Accuracy (b) Macro-average
Experiments to change the number of participants. (a) Accuracy (b) Macro-average

Fig. 6

Average variance between various parameters of participants in federated learning
Average variance between various parameters of participants in federated learning

Fig. 7

Running time experiment. (a) Change batch size (b) Change local epoch
Running time experiment. (a) Change batch size (b) Change local epoch
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