Defending federated learning systems against untargeted sybil attacks in non-IID environments
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 systems are vulnerable to Sybil attacks, where malicious clients inject multiple fake identities to corrupt the learning process. We propose mitigating untargeted Sybil attacks using a robust aggregation method that integrates FoolsGold with Sinkhorn-enhanced Earth Mover’s Distance (EMD) and a multi-step trust-weighting strategy. FoolsGold assigns trust scores based on client update similarity, while Sinkhorn-enhanced EMD refines Sybil detection by computing transport distances between gradient distributions. These scores are dynamically adjusted using a performance-aware mechanism, incorporating clients’ reported distributed accuracy to penalize unreliable updates. Additionally, mild adaptive trimming filters out the lowest 10% of trust scores, reducing adversarial influence while preserving valuable client contributions. These enhancements make the proposed method resilient to Sybil attacks while ensuring efficient model convergence in non-IID (non-independent and identically distributed) data settings. Empirical evaluations demonstrate that our approach outperforms FoolsGold, reducing false positives and improving model robustness.
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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 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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