A framework for DP fine-tuning of MLLMs that prunes visual tokens before training and selectively applies noisy gradient updates to blocks with the largest norms, reporting modest utility and memory gains over DP-SGD.
Security and privacy challenges of large language models: A survey
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Dual-Priv Pruning : Efficient Differential Private Fine-Tuning in Multimodal Large Language Models
A framework for DP fine-tuning of MLLMs that prunes visual tokens before training and selectively applies noisy gradient updates to blocks with the largest norms, reporting modest utility and memory gains over DP-SGD.