  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%.
