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
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
The results show that plan-first execution combined with label-preserving persistence can substantially strengthen persistent LLM agents, while revealing an important security-utility tradeoff introduced by strict integrity enforcement.
Modality reliance in VLMs is not fixed, but varies across tasks, evidence structures, models, and evaluation settings, which shows that modality reliance in VLMs is not fixed, but varies across tasks, evidence structures, and evaluation settings.
