Federated Learning of Explainable Artificial Intelligence (FED-XAI): A Review-Reference-Cited by-同舟云学术

Federated Learning of Explainable Artificial Intelligence (FED-XAI): A Review

Published:2023 Issue: Volume: Page:318-326
ISSN:2367-3370
Container-title:Lecture Notes in Networks and Systems
language:
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Author:

López-Blanco Raúl^ORCID,Alonso Ricardo S.^ORCID,González-Arrieta Angélica^ORCID,Chamoso Pablo^ORCID,Prieto Javier^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-38333-5_32

Reference34 articles.

1. Adli, H.K., et al.: Recent advancements and challenges of AIoT application in smart agriculture: a review. Sensors (Basel) 23(7) (2023)

2. Angelov, P.P., Soares, E.A., Jiang, R., Arnold, N.I., Atkinson, P.M.: Explainable artificial intelligence: an analytical review. Wiley Interdisc. Rev.: Data Min. Knowl. Discov. 11(5), e1424 (2021)

3. Bárcena, J.L.C., et al.: Fed-XAI: federated learning of explainable artificial intelligence models (2022)

4. Bárcena, J.L.C., Ducange, P., Ercolani, A., Marcelloni, F., Renda, A.: An approach to federated learning of explainable fuzzy regression models. In: 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–8. IEEE (2022)

5. Bárcena, J.L.C., et al.: Towards trustworthy AI for QoE prediction in b5g/6g networks (2022)

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