DEFEND: Poisoned Model Detection and Malicious Client Exclusion Mechanism for Secure Federated Learning-based Road Condition Classification
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918234415955968 |
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| author | Liu, Sheng Papadimitratos, Panos |
| author_facet | Liu, Sheng Papadimitratos, Panos |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_06172 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DEFEND: Poisoned Model Detection and Malicious Client Exclusion Mechanism for Secure Federated Learning-based Road Condition Classification Liu, Sheng Papadimitratos, Panos Cryptography and Security Artificial Intelligence 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. |
| title | DEFEND: Poisoned Model Detection and Malicious Client Exclusion Mechanism for Secure Federated Learning-based Road Condition Classification |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2512.06172 |