Multilingual Phishing Email Detection Using Lightweight Federated Learning

Abstract

Given the escalating global threat of phishing emails, it is imperative to develop effective solutions to mitigate their potentially devastating impacts on society. This study endeavours to construct a federated multilingual spam detection system employing logistic regression, specifically targeting English, French, and Russian emails. This is the first work to the best of our knowledge which considers a non-deep learning setting for federated learning, and combines federated learning with multilingual phishing detection. Evaluation of the models is based on accuracy metrics which are compared with a most frequent class baseline. Our findings indicate that an optimal configuration comprises 10 clients undergoing 100 epochs of training with 100 rounds of federated learning, resulting in superior performance. Notably, this approach significantly outperforms the baseline, achieving an accuracy of 89.46% compared to 70%