DEFEND: Poisoned Model Detection and Malicious Client Exclusion Mechanism for Secure Federated Learning-based Road Condition Classification

• URL: http://arxiv.org/abs/2512.06172v1
• Date: Fri, 05 Dec 2025 21:50:27 GMT
• Title: DEFEND: Poisoned Model Detection and Malicious Client Exclusion Mechanism for Secure Federated Learning-based Road Condition Classification
• Authors: Sheng Liu, Panos Papadimitratos,
• Abstract summary: Federated Learning (FL) has drawn the attention of the Intelligent Transportation Systems (ITS) community.<n>FL can train various models for ITS tasks, notably camera-based Road Condition Classification (RCC)<n>We propose DEFEND, which includes a poisoned model detection strategy.
• Score: 16.871281029100313
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Federated Learning (FL) has drawn the attention of the Intelligent Transportation Systems (ITS) community. FL can train various models for ITS tasks, notably camera-based Road Condition Classification (RCC), in a privacy-preserving collaborative way. However, opening up to collaboration also opens FL-based RCC systems to adversaries, i.e., misbehaving participants that can launch Targeted Label-Flipping Attacks (TLFAs) and threaten transportation safety. Adversaries mounting TLFAs poison training data to misguide model predictions, from an actual source class (e.g., wet road) to a wrongly perceived target class (e.g., dry road). Existing countermeasures against poisoning attacks cannot maintain model performance under TLFAs close to the performance level in attack-free scenarios, because they lack specific model misbehavior detection for TLFAs and neglect client exclusion after the detection. To close this research gap, we propose DEFEND, which includes a poisoned model detection strategy that leverages neuron-wise magnitude analysis for attack goal identification and Gaussian Mixture Model (GMM)-based clustering. DEFEND discards poisoned model contributions in each round and adapts accordingly client ratings, eventually excluding malicious clients. Extensive evaluation involving various FL-RCC models and tasks shows that DEFEND can thwart TLFAs and outperform seven baseline countermeasures, with at least 15.78% improvement, with DEFEND remarkably achieving under attack the same performance as in attack-free scenarios.

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