Abstract
Federated Learning has emerged as a promising approach to train machine learning models on decentralized data sources while preserving data privacy. This paper proposes a new federated approach for Naive Bayes (NB) classification, assuming discrete variables. Our approach federates a discriminative variant of NB, sharing meaningless parameters instead of conditional probability tables. Therefore, this process is more reliable against possible attacks. We conduct extensive experiments on 12 datasets to validate the efficacy of our approach, comparing federated and non-federated settings. Additionally, we benchmark our method against the generative variant of NB, which serves as a baseline for comparison. Our experimental results demonstrate the effectiveness of our method in achieving accurate classification.
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Notes
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Horizontally partitioned data refers to data divided by instances (rows). In contrast, vertically partitioned data refers to data divided by features (columns).
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The House Votes 84 database does not have results for 100 clients because it has 435 instances, and 500 are required to perform the 5-cv on each client.
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Acknowledgements
This work is partially funded by the following projects: TED2021-131291B-I00 (MICIU/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR), SBPLY/21/180225/000062 (Junta de Comunidades de Castilla-La Mancha and ERDF A way of making Europe), PID2022-139293NB-C32 (MICIU/AEI/10.13039/501100011033 and ERDF, EU), FPU21/01074 (MICIU/AEI/10.13039/501100011033 and ESF+); 2022-GRIN-34437 (Universidad de Castilla-La Mancha and ERDF A way of making Europe).
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Torrijos, P., Alfaro, J.C., Gámez, J.A., Puerta, J.M. (2025). Federated Learning with Discriminative Naive Bayes Classifier. In: Julian, V., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2024. IDEAL 2024. Lecture Notes in Computer Science, vol 15347. Springer, Cham. https://doi.org/10.1007/978-3-031-77738-7_27
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