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He-Qun Xian

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Preprint Aug 2026

Why Are LLM Backdoor Defenses Fragmented? A Feature-Level Explanation with Sparse Autoencoders

Results show that SAE-based analysis can explain defense fragmentation and guide interpretable backdoor mitigation and show system?atic encoding differences: dirty-label backdoors are dominated by isolated interaction features, whereas clean-label backdoors rely more on heterogeneous mixtures of mixed and weight-modifi...

Yikun Zeng, Chenxu Niu, Wei Zhang et al. · 0 citations
2026

MsaaDI: A Heterogeneity-Resilient Federated Learning Framework for IoT Device Identification With Multi-Scale Adaptive Aggregation

Accurate IoT device identification is critical to network forensics, access control, and anomaly detection in large-scale, security-sensitive environments. Federated learning (FL) provides a decentralized and privacy-enhancing approach well suited to IoT, but FL-based identification remains hampered by cross-client dat...

Tong Sun, Qian Lu, Hanlin Zhang et al. · 0 citations

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