Securing Multi-Layer Federated Learning: Detecting and Mitigating Adversarial Attacks
Bibliographic record
Abstract
Adversarial entities can poison models, slowing down or destroying the training process of federated learning (FL). Therefore, attack prevention and mitigation are crucial for FL. While existing works primarily address attack mitigation in conventional two-layer FL systems, research on attack prevention and mitigation in multi-layer federated learning systems remains limited. In this work, we propose new methods for anomaly detection and removal of attackers from training in a multi-layer FL system. First, we deploy a variational autoencoder (VAE) to detect which edge servers at the cohort level contain malicious clients. Subsequently, we devise two malicious client exclusion strategies: scoring-based and Bayesian-based, aimed at mitigating potential harm caused by these clients during model training. The experimental results demonstrate the superiority of our proposed methods over the previous work under a variety of scenarios.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".