用可区分的朴素贝叶斯实现隐私保护的联邦分类
Federated Learning with Discriminative Naive Bayes Classifier
- 用无意义参数代替条件概率表进行联邦学习
- 在12个数据集上达到与非联邦方法相当的准确率
- 相比生成式朴素贝叶斯更抗攻击，适合隐私敏感场景
联邦学习作为一种在去中心化数据源上训练模型并保护数据隐私的有前景方法，被广泛关注。本文提出一种针对离散变量的朴素贝叶斯（NB）分类的新联邦学习方法。该方法采用可区分的朴素贝叶斯变体，通过共享无意义参数而非条件概率表来实现联邦训练，从而提升对潜在攻击的鲁棒性。我们在12个数据集上进行了大量实验，验证了该方法的有效性，并对比了联邦与非联邦设置下的表现。此外，还将本方法与生成式朴素贝叶斯变体（作为基线）进行了比较。实验结果表明，该方法在实现高精度分类方面具有显著有效性。
原文摘要 · Abstract (English)
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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