# Federated Phish Bowl: LSTM-Based Decentralized Phishing Email Detection

arXiv: https://arxiv.org/abs/2110.06025 · HTML: https://mlsanity.com/paper/2110.06025/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 7 of 7)

- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism](https://mlsanity.com/paper/1911.05642/) (2019)
- [Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness](https://mlsanity.com/paper/2108.03980/) (2021)
- [Evaluation of Federated Learning in Phishing Email Detection](https://mlsanity.com/paper/2007.13300/) (2020)
- [A Deep Learning Model with Hierarchical LSTMs and Supervised Attention for Anti-Phishing](https://mlsanity.com/paper/1805.01554/) (2018)

## Built on by (most cited first, 2 of 2)

- [AdaPhish: AI-Powered Adaptive Defense and Education Resource Against Deceptive Emails](https://mlsanity.com/paper/2502.03622/) (2025)
- [Role-Aware Multi-modal federated learning system for detecting phishing webpages](https://mlsanity.com/paper/2509.22369/) (2025)

## Abstract (arXiv)

With increasingly more sophisticated phishing campaigns in recent years, phishing emails lure people using more legitimate-looking personal contexts. To tackle this problem, instead of traditional heuristics-based algorithms, more adaptive detection systems such as natural language processing (NLP)-powered approaches are essential to understanding phishing text representations. Nevertheless, concerns surrounding the collection of phishing data that might cover confidential information hinder the effectiveness of model learning. We propose a decentralized phishing email detection framework called Federated Phish Bowl (FedPB) which facilitates collaborative phishing detection with privacy. In particular, we devise a knowledge-sharing mechanism with federated learning (FL). Using long short-term memory (LSTM) for phishing detection, the framework adapts by sharing a global word embedding matrix across the clients, with each client running its local model with Non-IID data. We collected the most recent phishing samples to study the effectiveness of the proposed method using different client numbers and data distributions. The results show that FedPB can attain a competitive performance with a centralized phishing detector, with generality to various cases of FL retaining a prediction accuracy of 83%.