  Web phishing poses a dynamic threat, requiring detection systems to quickly
adapt to the latest tactics. Traditional approaches of accumulating data and
periodically retraining models are outpaced. We propose a novel paradigm
combining federated learning and continual learning, enabling distributed nodes
to continually update models on streams of new phishing data, without
accumulating data. These locally adapted models are then aggregated at a
central server via federated learning. To enhance detection, we introduce a
custom attention-based classifier model with residual connections, tailored for
web phishing, leveraging attention mechanisms to capture intricate phishing
patterns. We evaluate our hybrid learning paradigm across continual learning
strategies (cumulative, replay, MIR, LwF) and model architectures through an
empirical investigation. Our main contributions are: (1) a new hybrid
federated-continual learning paradigm for robust web phishing detection, and
(2) a novel attention + residual connections based model explicitly designed
for this task, attaining 0.93 accuracy, 0.90 precision, 0.96 recall and 0.93
f1-score with the LwF strategy, outperforming traditional approaches in
detecting emerging phishing threats while retaining past knowledge.
