A Novel Federated Learning Approach to Enable Distributed and Collaborative Genetic Programming
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gp-bibliography.bib Revision:1.8098
- @InProceedings{ribeiro:2023:EPIA,
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author = "Bruno Ribeiro and Luis Gomes and Zita Vale",
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title = "A Novel Federated Learning Approach to Enable
Distributed and Collaborative Genetic Programming",
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booktitle = "EPIA Conference on Artificial Intelligence",
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year = "2023",
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editor = "Nuno Moniz and Zita Vale and Jose Cascalho and
Catarina Silva and Raquel Sebastiao",
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volume = "14116",
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series = "LNCS",
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pages = "195--206",
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address = "Faial Island, Azores, Portugal",
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month = "5-8 " # sep,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-031-49011-8",
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URL = "http://link.springer.com/chapter/10.1007/978-3-031-49011-8_16",
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DOI = "doi:10.1007/978-3-031-49011-8_16",
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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 1.67percent, better than the scenario without
federated learning, that had an error rate of
3.33percent, considering a configuration with three
clients with different datasets each.",
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notes = "GECAD Research Group On Intelligent Engineering and
Computing for Advanced Innovation and Development, LASI
Intelligent Systems Associate Laboratory, Polytechnic
of Porto",
- }
Genetic Programming entries for
Bruno Ribeiro
Luis Gomes
Zita Vale
Citations