Role-Aware Multi-modal federated learning system for detecting phishing webpages
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914096543170560 |
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| author | Wang, Bo Khan, Imran White, Martin Beloff, Natalia |
| author_facet | Wang, Bo Khan, Imran White, Martin Beloff, Natalia |
| contents | We present a federated, multi-modal phishing website detector that supports URL, HTML, and IMAGE inputs without binding clients to a fixed modality at inference: any client can invoke any modality head trained elsewhere. Methodologically, we propose role-aware bucket aggregation on top of FedProx, inspired by Mixture-of-Experts and FedMM. We drop learnable routing and use hard gating (selecting the IMAGE/HTML/URL expert by sample modality), enabling separate aggregation of modality-specific parameters to isolate cross-embedding conflicts and stabilize convergence. On TR-OP, the Fusion head reaches Acc 97.5% with FPR 2.4% across two data types; on the image subset (ablation) it attains Acc 95.5% with FPR 5.9%. For text, we use GraphCodeBERT for URLs and an early three-way embedding for raw, noisy HTML. On WebPhish (HTML) we obtain Acc 96.5% / FPR 1.8%; on TR-OP (raw HTML) we obtain Acc 95.1% / FPR 4.6%. Results indicate that bucket aggregation with hard-gated experts enables stable federated training under strict privacy, while improving the usability and flexibility of multi-modal phishing detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22369 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Role-Aware Multi-modal federated learning system for detecting phishing webpages Wang, Bo Khan, Imran White, Martin Beloff, Natalia Machine Learning Distributed, Parallel, and Cluster Computing We present a federated, multi-modal phishing website detector that supports URL, HTML, and IMAGE inputs without binding clients to a fixed modality at inference: any client can invoke any modality head trained elsewhere. Methodologically, we propose role-aware bucket aggregation on top of FedProx, inspired by Mixture-of-Experts and FedMM. We drop learnable routing and use hard gating (selecting the IMAGE/HTML/URL expert by sample modality), enabling separate aggregation of modality-specific parameters to isolate cross-embedding conflicts and stabilize convergence. On TR-OP, the Fusion head reaches Acc 97.5% with FPR 2.4% across two data types; on the image subset (ablation) it attains Acc 95.5% with FPR 5.9%. For text, we use GraphCodeBERT for URLs and an early three-way embedding for raw, noisy HTML. On WebPhish (HTML) we obtain Acc 96.5% / FPR 1.8%; on TR-OP (raw HTML) we obtain Acc 95.1% / FPR 4.6%. Results indicate that bucket aggregation with hard-gated experts enables stable federated training under strict privacy, while improving the usability and flexibility of multi-modal phishing detection. |
| title | Role-Aware Multi-modal federated learning system for detecting phishing webpages |
| topic | Machine Learning Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2509.22369 |