Defending federated learning systems against untargeted sybil attacks in non-IID environments
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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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".