Split Federated Learning (SFL) reduces client-side computation by partitioning neural networks between clients and a server, but its cut-layer interaction also creates a new surface for backdoor injection. Existing attacks often rely on persistent poisoning or privileged server-side knowledge, limiting their practicali...
Zheng-Yuan Jiang, S. Balaji, Yi-Min Chen et al.· IEEE Conference on Communica...· 0 citations
Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for improving the quality of generated contents of Large Language Models (LLMs) by grounding responses in external knowledge, thus reducing hallucinations and factual errors. However, recent studies have highlighted a critical vulnerability: advers...
Xingyu Lyu, Jia-Yi Wang, Jian-Feng He et al.· 0 citations
This work presents the first systematic, bidirectional study of cross-modal unlearning transfer across three VLM architectures: LLaVA-1.5 (MLP projection), InstructBLIP (Q-Former), and IDEFICS (gated cross-attention), finding that unlearning transfers across modalities, but the transfer is asymmetric and incomplete.
S. Balaji, Yili Ren, Guangjing Wang et al.· arXiv.org· 0 citations
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