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

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Distributed Computing and Artificial Intelligence, 20th International Conference (DCAI 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 740))

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Abstract

The arrival of a new wave of popularity in the field of Artificial Intelligence has again highlighted that this is a complex field, with issues to be solved and many approaches involving ethical, moral and even other issues concerning privacy, security or copyright. Some of these issues are being addressed by new approaches to Artificial Intelligence towards explainable and/or trusted AI and new distributed learning architectures such as Federated Learning. Explainable AI provides transparency and understanding in decision-making processes, which is essential to establish trust and acceptance of AI systems in different sectors. Furthermore, Federated Learning enables collaborative training of AI models without compromising data privacy, facilitating cooperation and advancement in sensitive environments. Through this study we aim to conduct a review of a new approach called FED-XAI that brings together explainable AI and Federated Learning and that has emerged as a new integrative approach to AI recently. Thanks to this review, it is concluded that the FED-XAI is a field with recent experimental results and that it is booming thanks to European projects, which are championing the use of this approach.

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Acknowledgements

This work has been partially supported by the project TED2021-132339B-C43 (idrECO), funded by MCIN/AEI/10.13039/501100011033 and by the European Union “NextGenerationEU”/PRTR.

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Authors and Affiliations

  1. BISITE Research Group, University of Salamanca, Edificio Multiusos I+D+i, Calle Espejo 2, 37007, Salamanca, Spain

    Raúl López-Blanco, Angélica González-Arrieta, Pablo Chamoso & Javier Prieto

  2. AIR Institute - Deep Tech Lab, IoT Digital Innovation Hub, Salamanca, Spain

    Ricardo S. Alonso

  3. UNIR, International University of La Rioja, Av. de la Paz, 137, 26006, Logroño, La Rioja, Spain

    Ricardo S. Alonso

Authors
  1. Raúl López-Blanco
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  2. Ricardo S. Alonso
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  3. Angélica González-Arrieta
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  4. Pablo Chamoso
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  5. Javier Prieto
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Corresponding author

Correspondence to Raúl López-Blanco .

Editor information

Editors and Affiliations

  1. ETSII / CETINIA, Universidad Rey Juan Carlos, Madrid, Madrid, Spain

    Sascha Ossowski

  2. Politechnika Śląska Kielce, Kielce University of Technology, Santa Cruz, Poland

    Pawel Sitek

  3. Department of Informatics, Engineering School, University of Minho, Braga, Portugal

    Cesar Analide

  4. Departamento de Engenharia Informática, Polytechnic Institute of Porto, Porto, Portugal

    Goreti Marreiros

  5. BISITE, University of Salamanca, Salamanca, Salamanca, Spain

    Pablo Chamoso

  6. BISITE, University of Salamanca, Salamanca, Salamanca, Spain

    Sara Rodríguez

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López-Blanco, R., Alonso, R.S., González-Arrieta, A., Chamoso, P., Prieto, J. (2023). Federated Learning of Explainable Artificial Intelligence (FED-XAI): A Review. In: Ossowski, S., Sitek, P., Analide, C., Marreiros, G., Chamoso, P., Rodríguez, S. (eds) Distributed Computing and Artificial Intelligence, 20th International Conference. DCAI 2023. Lecture Notes in Networks and Systems, vol 740. Springer, Cham. https://doi.org/10.1007/978-3-031-38333-5_32

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