Multilingual Phishing Email Detection Using Lightweight Federated Learning
Bibliographic record
Abstract
Given the escalating global threat of phishing emails, it is imperative to develop effective solutions to mitigate their potentially devastating impacts on society. This study endeavours to construct a federated multilingual spam detection system employing logistic regression, specifically targeting English, French, and Russian emails. This is the first work to the best of our knowledge which considers a non-deep learning setting for federated learning, and combines federated learning with multilingual phishing detection. Evaluation of the models is based on accuracy metrics which are compared with a most frequent class baseline. Our findings indicate that an optimal configuration comprises 10 clients undergoing 100 epochs of training with 100 rounds of federated learning, resulting in superior performance. Notably, this approach significantly outperforms the baseline, achieving an accuracy of $89.46 \%$ compared to $70 \%$.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".