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A Novel Federated Learning Approach to Enable Distributed and Collaborative Genetic Programming

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Progress in Artificial Intelligence (EPIA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14116))

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Abstract

The combination of genetic programming with federated learning could solve the computational distribution while promoting a collaborative learning environment. This paper proposes a federated learning configuration that enables the use of genetic programming for its global model. In addition, this paper also proposes a new aggregation algorithm that enables the collaborative evolution of genetic programming individuals in federated learning. The case study uses flexible genetic programming, an existing and successful algorithm for image classification, integrated into a federated learning framework. The results show that the use of genetic programming with federated learning achieved a classification error rate of 1.67%, better than the scenario without federated learning, that had an error rate of 3.33%, considering a configuration with three clients with different datasets each.

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Acknowledgements

This article is a result of the project RETINA (NORTE-01-0145-FEDER-000062), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). The authors acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team.

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Authors and Affiliations

  1. GECAD Research Group On Intelligent Engineering and Computing for Advanced Innovation and Development, LASI—Intelligent Systems Associate Laboratory, Polytechnic of Porto, R. Dr. António Bernardino de Almeida, 431, 4200-072, Porto, Portugal

    Bruno Ribeiro, Luis Gomes & Zita Vale

Authors
  1. Bruno Ribeiro
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  2. Luis Gomes
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  3. Zita Vale
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Corresponding author

Correspondence to Luis Gomes .

Editor information

Editors and Affiliations

  1. Lucy Family Institute for Data and Society, Notre Dame, IN, USA

    Nuno Moniz

  2. GECAD, Polytechnic of Porto, Porto, Portugal

    Zita Vale

  3. GRIA—LIACC, University of Azores, Ponta Delgada, Portugal

    José Cascalho

  4. CISUC, University of Coimbra, Coimbra, Portugal

    Catarina Silva

  5. IEETA, University of Aveiro, Aveiro, Portugal

    Raquel Sebastião

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Ribeiro, B., Gomes, L., Vale, Z. (2023). A Novel Federated Learning Approach to Enable Distributed and Collaborative Genetic Programming. In: Moniz, N., Vale, Z., Cascalho, J., Silva, C., Sebastião, R. (eds) Progress in Artificial Intelligence. EPIA 2023. Lecture Notes in Computer Science(), vol 14116. Springer, Cham. https://doi.org/10.1007/978-3-031-49011-8_16

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  • DOI: https://doi.org/10.1007/978-3-031-49011-8_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-49010-1

  • Online ISBN: 978-3-031-49011-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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