FedTrident: Resilient Road Condition Classification Against Poisoning Attacks in Federated Learning

• URL: http://arxiv.org/abs/2603.19101v1
• Date: Thu, 19 Mar 2026 16:26:13 GMT
• Title: FedTrident: Resilient Road Condition Classification Against Poisoning Attacks in Federated Learning
• Abstract summary: FL has emerged as a transformative paradigm for ITS, notably camera-based Road Condition Classification (RCC)<n>RCC exposes the system to adversarial participants launching Targeted Label-Flipping Attacks (TLFAs)<n>We propose FedTrident, which can effectively thwart TLFAs, achieving performance comparable to that in attack-free scenarios.
• Score: 16.871281029100313
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: FL has emerged as a transformative paradigm for ITS, notably camera-based Road Condition Classification (RCC). However, by enabling collaboration, FL-based RCC exposes the system to adversarial participants launching Targeted Label-Flipping Attacks (TLFAs). Malicious clients (vehicles) can relabel their local training data (e.g., from an actual uneven road to a wrong smooth road), consequently compromising global model predictions and jeopardizing transportation safety. Existing countermeasures against such poisoning attacks fail to maintain resilient model performance near the necessary attack-free levels in various attack scenarios due to: 1) not tailoring poisoned local model detection to TLFAs, 2) not excluding malicious vehicular clients based on historical behavior, and 3) not remedying the already-corrupted global model after exclusion. To close this research gap, we propose FedTrident, which introduces: 1) neuron-wise analysis for local model misbehavior detection (notably including attack goal identification, critical feature extraction, and GMM-based model clustering and filtering); 2) adaptive client rating for client exclusion according to the local model detection results in each FL round; and 3) machine unlearning for corrupted global model remediation once malicious clients are excluded during FL. Extensive evaluation across diverse FL-RCC models, tasks, and configurations demonstrates that FedTrident can effectively thwart TLFAs, achieving performance comparable to that in attack-free scenarios and outperforming eight baseline countermeasures by 9.49% and 4.47% for the two most critical metrics. Moreover, FedTrident is resilient to various malicious client rates, data heterogeneity levels, complicated multi-task, and dynamic attacks.

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