FedTrident: Resilient Road Condition Classification Against Poisoning Attacks in Federated Learning
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...
| Authors | Sheng Liu, Panos Papadimitratos |
|---|---|
| Primary category | cs.CR |
| Published | 19 March 2026 |
| Last updated at source | 19 March 2026 |
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