# Evaluation of Federated Learning in Phishing Email Detection

arXiv: https://arxiv.org/abs/2007.13300 · HTML: https://mlsanity.com/paper/2007.13300/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2020.

## Built on by (most cited first, 6 of 6)

- [An Explainable Transformer-based Model for Phishing Email Detection: A Large Language Model Approach](https://mlsanity.com/paper/2402.13871/) (2024)
- [Novel Interpretable and Robust Web-based AI Platform for Phishing Email Detection](https://mlsanity.com/paper/2405.11619/) (2024)
- [Federated Phish Bowl: LSTM-Based Decentralized Phishing Email Detection](https://mlsanity.com/paper/2110.06025/) (2021)
- [Which PPML Would a User Choose? A Structured Decision Support Framework for Developers to Rank PPML Techniques Based on User Acceptance Criteria](https://mlsanity.com/paper/2411.06995/) (2024)
- [EvoMail: Self-Evolving Cognitive Agents for Adaptive Spam and Phishing Email Defense](https://mlsanity.com/paper/2509.21129/) (2025)
- [Role-Aware Multi-modal federated learning system for detecting phishing webpages](https://mlsanity.com/paper/2509.22369/) (2025)

## Abstract (arXiv)

The use of Artificial Intelligence (AI) to detect phishing emails is primarily dependent on large-scale centralized datasets, which opens it up to a myriad of privacy, trust, and legal issues. Moreover, organizations are loathed to share emails, given the risk of leakage of commercially sensitive information. So, it is uncommon to obtain sufficient emails to train a global AI model efficiently. Accordingly, privacy-preserving distributed and collaborative machine learning, particularly Federated Learning (FL), is a desideratum. Already prevalent in the healthcare sector, questions remain regarding the effectiveness and efficacy of FL-based phishing detection within the context of multi-organization collaborations. To the best of our knowledge, the work herein is the first to investigate the use of FL in email anti-phishing. This paper builds upon a deep neural network model, particularly RNN and BERT for phishing email detection. It analyzes the FL-entangled learning performance under various settings, including balanced and asymmetrical data distribution. Our results corroborate comparable performance statistics of FL in phishing email detection to centralized learning for balanced datasets, and low organization counts. Moreover, we observe a variation in performance when increasing organizational counts. For a fixed total email dataset, the global RNN based model suffers by a 1.8% accuracy drop when increasing organizational counts from 2 to 10. In contrast, BERT accuracy rises by 0.6% when going from 2 to 5 organizations. However, if we allow increasing the overall email dataset with the introduction of new organizations in the FL framework, the organizational level performance is improved by achieving a faster convergence speed. Besides, FL suffers in its overall global model performance due to highly unstable outputs if the email dataset distribution is highly asymmetric.