Federated Learning with Discriminative Naive Bayes Classifier

TL;DR

This paper introduces a federated learning method for a discriminative Naive Bayes classifier that enhances privacy and robustness, validated through extensive experiments on multiple datasets.

Contribution

It presents a novel federated approach for discriminative Naive Bayes that shares meaningless parameters, improving privacy and security over traditional methods.

Findings

Effective classification accuracy demonstrated on 12 datasets

Outperforms generative Naive Bayes in federated settings

More robust against potential attacks due to parameter sharing strategy

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.