A Stability-Enhanced Dynamic Backdoor Defense in Federated Learning for IIoT
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Federated learning (FL) systems enable collaborative model training among industrial Internet of Things (IIoT) devices but face significant security challenges, particularly in backdoor attacks, due to the nonindependent and identically distributed (non-IID) nature of data. To address this challenge, we propose a stability-enhanced dynamic backdoor defense approach in FL for IIoT, which maintains primary task accuracy while strengthening defenses in non-IID environments. Leveraging the similarity between data distribution and model updates, we segment non-IID scenarios into multiple quasi-IID environments. Our approach includes a dynamic client matching module, a malicious filtering module, and robust personalized aggregation to reduce the success rate of backdoor attacks while augmenting the resilience and precision of the aggregated model. The effectiveness of our strategy has been validated through analyses on the Modified National Institute of Standards and Technology database (MNIST), Canadian Institute for Advanced Research, 10 classes (CIFAR-10), Internet of Things (IoT)-23, and Washington University in St. Louis (WUSTL)-IIOT datasets in both IID and non-IID scenarios. Notably, on the IoT-23 and WUSTL-IIOT, the success rate of backdoor attacks was significantly reduced to 3.46%.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it