Going Haywire: False Friends in Federated Learning and How to Find Them
Bibliographic record
Abstract
Federated Learning (FL) promises to offer a major paradigm shift in the way deep learning models are trained at scale, yet malicious clients can surreptitiously embed backdoors into models via trivial augmentation on their own subset of the data. This is especially true in small- and medium-scale FL systems, which consist of dozens, rather than millions, of clients. In this work, we investigate a novel attack scenario for an FL architecture consisting of multiple non-i.i.d. silos of data in which each distribution has a unique backdoor attacker and where the model convergences of adversaries are not more similar than those of benign clients. We propose a new method, dubbed Haywire, as a security-in-depth approach to respond to this novel attack scenario. Our defense utilizes a combination of kPCA dimensionality reduction of fully-connected layers in the network, KMeans anomaly detection to drop anomalous clients, and server aggregation robust to outliers via the Geometric Median. Our solution prevents the contamination of the global model despite having no access to the backdoor triggers. We evaluate the performance of Haywire from model-accuracy, defense-performance, and attack-success perspectives against multiple baselines. Through an extensive set of experiments, we find that Haywire produces the best performances at preventing backdoor attacks while simultaneously not unfairly penalizing benign clients. We carried out additional in-depth experiments across multiple runs that demonstrate the reliability of Haywire.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.016 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".