FedITD: A Federated Parameter-Efficient Tuning With Pre-Trained Large Language Models and Transfer Learning Framework for Insider Threat Detection
Bibliographic record
Abstract
Insider threats cause greater losses than external attacks, prompting organizations to invest in detection systems. However, there exist challenges: 1) Security and privacy concerns prevent data sharing, making it difficult to train robust models and identify new attacks. 2) The diversity and uniqueness of organizations require localized models, as a universal solution could be more effective. 3) High resource costs, delays, and data security concerns complicate building effective detection systems. This paper introduces FedITD, a flexible, hierarchy, and federated framework with local real-time detection systems, combining Large Language Models (LLM), Federated Learning (FL), Parameter Efficient Tuning (PETuning), and Transfer Learning (TF) for insider threat detection. FedITD uses FL to protect privacy while indirect integrating client information and employs PETuning methods (Adapter, BitFit, LoRA) with LLMs (BERT, RoBERTa, XLNet, DistilBERT) to reduce resource use and time delay. FedITD customizes client models and optimizes performance via transfer learning without central data transfer, further enhancing the detection of new attacks. FedITD outperforms other federated learning methods and its performance is very close to the best centrally trained method. Extensive experiment results show FedITD’s superior performance, adaptability to varied data, and reduction of resource costs, achieving an optimal balance in detection capabilities across source data, unlabeled local data, and global data. Alternative PETuning implementations are also explored in this paper.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".