Abstract
The privacy and security of users’ data have been a concern in the last few years. New techniques like federated learning have appeared to allow the training of machine learning models without sharing the users’ personal data. These systems have a lot of variables that can change the outcome of the models. Studies have been made to explore the effect of these variables and their importance. However, those studies only focus on machine learning models, namely deep learning. This paper explores the impact of the federation size in unbalanced data using a genetic programming model for image classification. The results show that the federation size can have impact on the contributions of each client, and the dataset size can influence the quality of the individuals.
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Acknowledgements
The present work has received funding from European Regional Development Fund through COMPETE 2020 - Operational Programme for Competitiveness and Internationalisation through the P2020 Project F4iTECH (ANI|P2020 POCI-01-0247-FEDER-181419), and has been developed under the EUREKA - CELTIC-NEXT Project F4iTECH (C2021/1-10), we also acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team.
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Ribeiro, B., Gomes, L., Faia, R., Vale, Z. (2023). Federated Genetic Programming: A Study About the Effects of Non-IID and Federation Size. 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_20
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