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

Source: arXiv - 2603.19101v1

Overview

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.

Key Contributions

This paper presents research in the following areas:

• cs.CR
• cs.AI
• cs.DC
• cs.LG

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of cs.CR.

Authors

• Sheng Liu
• Panos Papadimitratos

Paper Information

• arXiv ID: 2603.19101v1
• Categories: cs.CR, cs.AI, cs.DC, cs.LG
• Published: March 19, 2026
• PDF: Download PDF