Federated Few-Shot Learning for Robust and Privacy-Driven Network Intrusion Detection in IoT
Bibliographic record
Abstract
With the rapid growth of IoT networks, traditional centralized Network Intrusion Detection Systems (NIDS) face challenges in maintaining data privacy while processing large volumes of data. Federated Learning (FL) enhances privacy by enabling distributed learning without sharing raw data. However, its effectiveness is limited by the scarcity of labeled attack data. To address this, we propose a Federated Few-Shot Learning (FFSL) framework for IoT network intrusion detection. The framework integrates Few-Shot Learning (FSL) to improve NIDS generalization from a few labeled examples. Meanwhile, FL facilitates collaborative training across distributed IoT devices while preserving privacy. The proposed approach employs a metric-based method combined with Long Short-Term Memory (LSTM) networks to capture temporal dependencies in network traffic, enhancing detection accuracy. The proposed technique is evaluated on the Telemetry of services, Operating systems, and Network traffic dataset for the IoT (ToN_IoT) dataset, the method achieves higher precision, recall, and F1-score compared to state-of-the-art techniques while ensuring computational efficiency and data privacy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".