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This research introduces a federated learning framework for Averaged $n$-Dependence Estimators (AnDE) that enhances privacy by preventing the transmission of semantically meaningful parameters during aggregation. Experimental results on discrete datasets demonstrate that these discriminative models outperform standard Naive Bayes approaches while maintaining accuracy even when differential privacy is applied. The work validates the continued relevance of probabilistic models within modern distributed machine learning architectures.
Federated Learning of AnDE Classifiers
arXiv:2609.28695v1 Announce Type: new Abstract: This work presents a federated framework for training Averaged $n$-Dependence Estimators (AnDE) in distributed environments. The proposed method focuses on the discriminative setting, where model weights are learned locally and aggregated globally, supporting any dependency order $n$. This design allows federated training without transmitting semantically meaningful parameters, improving privacy. Additionally, generative AnDE models are federated to provide a comparative baseline, with optional differential privacy applied to the aggregation of probability tables. Experiments on 12 discrete datasets show that discriminative models with $n \geq 1$ consistently outperform federated Naive Bayes (NB, $n=0$), and that privacy-preserving aggregation is effective with limited accuracy loss. These results establish federated AnDE as a viable and privacy-preserving framework, showing that probabilistic models remain applicable in modern federated learning settings.
Abstract:This work presents a federated framework for training Averaged $n$-Dependence Estimators (AnDE) in distributed environments. The proposed method focuses on the discriminative setting, where model weights are learned locally and aggregated globally, supporting any dependency order $n$. This design allows federated training without transmitting semantically meaningful parameters, improving privacy. Additionally, generative AnDE models are federated to provide a comparative baseline, with optional differential privacy applied to the aggregation of probability tables. Experiments on 12 discrete datasets show that discriminative models with $n \geq 1$ consistently outperform federated Naive Bayes (NB, $n=0$), and that privacy-preserving aggregation is effective with limited accuracy loss. These results establish federated AnDE as a viable and privacy-preserving framework, showing that probabilistic models remain applicable in modern federated learning settings.
Machine Learning (cs.LG)
arXiv:2609.28695 [cs.LG]
(or arXiv:2609.28695v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.28695 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
ECML PKDD 2025 Workshops, Communications in Computer and Information Science, vol 2841, pp. 464-471, Springer, Cham (2026)
https://doi.org/10.1007/978-3-032-19102-1_28 Focus to learn more DOI(s) linking to related resources
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