AnDE分类器的联邦学习
Federated Learning of AnDE Classifiers
- Universidad de Castilla-La Mancha（卡斯蒂利亚-拉曼恰大学）
- Instituto de Investigación en Informática de Albacete（阿尔瓦塞特信息学研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出联邦学习框架训练AnDE分类器，支持任意依赖阶数，通过本地学习与全局聚合保护隐私，实验表明n≥1的判别模型优于联邦朴素贝叶斯，且隐私聚合损失有限。
AI中文摘要：
这项工作提出了一种在分布式环境中训练平均n依赖估计器（AnDE）的联邦框架。所提出的方法侧重于判别式设置，其中模型权重在本地学习并在全局聚合，支持任意依赖阶数n。这种设计允许在不传输语义上有意义的参数的情况下进行联邦训练，从而提高了隐私性。此外，生成式AnDE模型也被联邦化以提供比较基线，并对概率表的聚合应用了可选的差分隐私。在12个离散数据集上的实验表明，n≥1的判别式模型始终优于联邦朴素贝叶斯（NB，n=0），并且隐私保护聚合在有限的精度损失下是有效的。这些结果确立了联邦AnDE作为一种可行且保护隐私的框架，表明概率模型在现代联邦学习环境中仍然适用。
英文摘要：
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.
