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

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@InProceedings{ribeiro:2023:EPIA,

• author = "Bruno Ribeiro and Luis Gomes and Zita Vale",
• title = "A Novel Federated Learning Approach to Enable Distributed and Collaborative Genetic Programming",
• booktitle = "EPIA Conference on Artificial Intelligence",
• year = "2023",
• editor = "Nuno Moniz and Zita Vale and Jose Cascalho and Catarina Silva and Raquel Sebastiao",
• volume = "14116",
• series = "LNCS",
• pages = "195--206",
• address = "Faial Island, Azores, Portugal",
• month = "5-8 " # sep,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-031-49011-8",
• URL = "http://link.springer.com/chapter/10.1007/978-3-031-49011-8_16",
• DOI = "doi:10.1007/978-3-031-49011-8_16",
• 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.",
• 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