FALCON: Federated Anomaly Learning and Collaborative Network for Secure Autonomous Vehicles
Bibliographic record
Abstract
Federated learning (FL) has been developed as an effective method for privacy-preserving collaborative training across numerous clients. FL is becoming more popular in safetycritical sectors like autonomous vehicles (AVs) because it allows decentralized learning from various datasets while ensuring data privacy. However, its decentralized design makes it subject to adversarial threats, notably data poisoning techniques like LF, in which unscrupulous users modify training data to reduce model performance. This paper introduces FALCON (Federated Anomaly Learning and COllaborative Network), a novel defense framework designed to enhance FL security against LF attacks in AV-based systems. FALCON integrates Federated Anomaly Detection (FAD), with Principal Component Analysis (PCA), and Multi-Class Support Vector Machines (MCSVM). Our method uses statistical outlier identification, peer-to-peer anomaly validation, and server-level graph-based monitoring to systematically identify and mitigate adversarial updates before they compromise the global model. Experimental results demonstrate that our method significantly improves robustness, reducing accuracy degradation from 15% to less than 2% under high intensity poisoning. Furthermore, our defense mechanism reduces the attack success rate by over 90% at a poisoning level of α=0.9, outperforming state-of-the-art FL defenses.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".