Towards Well-trained Model Robustness in Federated Learning: An Adversarial- Example-Generation- Efficiency Perspective
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), as a privacy-oriented distributed machine learning paradigm, can obtain a well-trained global model without private dataset transferring. Nevertheless, FL is subject to severe security threats of adversarial examples (AEs) with unnoticeable perturbations, generated by white-box attacks in honest-but-curious FL participants. Adversarial training is an effective solution to enhance the robustness of the model by identifying AEs as correct samples. However, the AE training efficiency is crucial in realistic scenarios of adversarial training, such as autonomous driving. Researchers have proposed the Fast Gradient Sign Method (FGSM) and its improvement to generate AEs rapidly. In this paper, we propose a novel optimizer-based FGSM, FastAdaBelief-based FGSM (FAB-FGSM), in order to generate AEs more efficiently and effectively. Benefitting from time-vary coefficients and a vanishing factor, FAB-FGSM realizes a more adaptive iteration step size than AdaBelief-based FGSM (AB-FGSM) and Adam-based FGSM (AI-FGSM). We explore the probable causes by recalling the theoretical analysis of three optimizers. Extensive experiment results demonstrate that compared to AB-FGSM and AI-FGSM, our FAB-FGSM achieves the fastest convergence and the best attack success rate in four target models, including Inception v3, Inception v4, Inception ResNet v2, ResNet-101.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it