Towards Trustworthy Email Phishing Detection: Integrating Multi-Modal Deep Learning, Federated Learning, and Explainable AI

Adetoye A. Adeyemo, Ozichi Emuoyibofarhe, Adeyinka Abiodun, James Adegboye, Sunday Ajagbe
Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, PMLR 319:101-117, 2026.

Abstract

This research presents a robust phishing detection system integrating multi-modal deep learning with federated learning and explainable AI. The model combines email text analysis with URL structural features using embedding, LSTM, and feature fusion layers. In the centralised setup, the model achieved accuracy of 99.6%, precision of 99.8%, recall of 99.5%, F1-score of 99.72%, and AUC of 99.85%. The federated model maintained competitive performance while protecting user privacy. LIME and perturbation analysis reveal which word-level features drive phishing classification decisions. Federated learning offers a strong privacy-preserving alternative despite marginally lower metrics than centralised training.

Cite this Paper

BibTeX
@InProceedings{pmlr-v319-adeyemo26a, title = {Towards Trustworthy Email Phishing Detection: Integrating Multi-Modal Deep Learning, Federated Learning, and Explainable {AI}}, author = {Adeyemo, Adetoye A. and Emuoyibofarhe, Ozichi and Abiodun, Adeyinka and Adegboye, James and Ajagbe, Sunday}, booktitle = {Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments}, pages = {101--117}, year = {2026}, editor = {Folorunso, Sakinat and Ogundokun, Roseline and Oladipo, Francisca}, volume = {319}, series = {Proceedings of Machine Learning Research}, month = {11--14 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v319/main/assets/adeyemo26a/adeyemo26a.pdf}, url = {https://proceedings.mlr.press/v319/adeyemo26a.html}, abstract = {This research presents a robust phishing detection system integrating multi-modal deep learning with federated learning and explainable AI. The model combines email text analysis with URL structural features using embedding, LSTM, and feature fusion layers. In the centralised setup, the model achieved accuracy of 99.6%, precision of 99.8%, recall of 99.5%, F1-score of 99.72%, and AUC of 99.85%. The federated model maintained competitive performance while protecting user privacy. LIME and perturbation analysis reveal which word-level features drive phishing classification decisions. Federated learning offers a strong privacy-preserving alternative despite marginally lower metrics than centralised training.} }
Endnote
%0 Conference Paper %T Towards Trustworthy Email Phishing Detection: Integrating Multi-Modal Deep Learning, Federated Learning, and Explainable AI %A Adetoye A. Adeyemo %A Ozichi Emuoyibofarhe %A Adeyinka Abiodun %A James Adegboye %A Sunday Ajagbe %B Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments %C Proceedings of Machine Learning Research %D 2026 %E Sakinat Folorunso %E Roseline Ogundokun %E Francisca Oladipo %F pmlr-v319-adeyemo26a %I PMLR %P 101--117 %U https://proceedings.mlr.press/v319/adeyemo26a.html %V 319 %X This research presents a robust phishing detection system integrating multi-modal deep learning with federated learning and explainable AI. The model combines email text analysis with URL structural features using embedding, LSTM, and feature fusion layers. In the centralised setup, the model achieved accuracy of 99.6%, precision of 99.8%, recall of 99.5%, F1-score of 99.72%, and AUC of 99.85%. The federated model maintained competitive performance while protecting user privacy. LIME and perturbation analysis reveal which word-level features drive phishing classification decisions. Federated learning offers a strong privacy-preserving alternative despite marginally lower metrics than centralised training.
APA
Adeyemo, A.A., Emuoyibofarhe, O., Abiodun, A., Adegboye, J. & Ajagbe, S.. (2026). Towards Trustworthy Email Phishing Detection: Integrating Multi-Modal Deep Learning, Federated Learning, and Explainable AI. Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, in Proceedings of Machine Learning Research 319:101-117 Available from https://proceedings.mlr.press/v319/adeyemo26a.html.

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