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<p>User profiles constructed using vast network behaviour data are widely used in various fields. However, data island and central server capacity problems limit the implementation of centralised big data training. This paper proposes a user profile construction method, Fed-UserPro, based on federated learning, which uses non-independent and identically distributed unstructured user text to jointly construct user profiles. Latent Dirichlet allocation model and softmax multi-classification regression method are introduced into the federated learning structure to train data. The results show that the accuracy of the Fed-UserPro method is 8.69%–19.71% higher than that of single-party machine learning methods.</p>
</abstract>

User profiles constructed using vast network behaviour data are widely used in various fields. However, data island and central server capacity problems limit the implementation of centralised big data training. This paper proposes a user profile construction method, Fed-UserPro, based on federated learning, which uses non-independent and identically distributed unstructured user text to jointly construct user profiles. Latent Dirichlet allocation model and softmax multi-classification regression method are introduced into the federated learning structure to train data. The results show that the accuracy of the Fed-UserPro method is 8.69%–19.71% higher than that of single-party machine learning methods.

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

Hebei University of Economics and Business

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Fed-UserPro: A user profile construction method based on federated learning"}

User profiles constructed using vast network behaviour data are widely used in various fields. However, data island and central server capacity problems limit...

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

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

Schlüsselwörter
Federated Learning, Non-independent and identically distributed, Multi-classification, User profile

© 2023 Yilin Fan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

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

SchlüsselwörterFederated Learning, Non-independent and identically distributed, Multi-classification, User profile

© 2023 Yilin Fan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Federated Learning, Non-independent and identically distributed, Multi-classification, User profile