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cs.CR2026
FedGraph: Defending Federated Large Language Model Fine-Tuning Against Backdoor Attacks via Graph-Based Aggregation
Xi Chen, Chunyi Zhou, Rui Zeng +3
Federated fine-tuning of large language models (LLMs) allows multiple clients to collaboratively adapt models without sharing raw data, offering strong privacy benefits. However, t…
cs.CR2024
CLIBE: Detecting Dynamic Backdoors in Transformer-based NLP Models
Rui Zeng, Xi Chen, Yuwen Pu +3
Backdoors can be injected into NLP models to induce misbehavior when the input text contains a specific feature, known as a trigger, which the attacker secretly selects. Unlike fix…
cs.CR2024
Enhancing Adversarial Transferability with Adversarial Weight Tuning
Jiahao Chen, Zhou Feng, Rui Zeng +6
Deep neural networks (DNNs) are vulnerable to adversarial examples (AEs) that mislead the model while appearing benign to human observers. A critical concern is the transferability…