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cs.CR2025
CryptPEFT: Efficient and Private Neural Network Inference via Parameter-Efficient Fine-Tuning
Saisai Xia, Wenhao Wang, Zihao Wang +4
Publicly available large pretrained models (i.e., backbones) and lightweight adapters for parameter-efficient fine-tuning (PEFT) have become standard components in modern machine l…
cs.CR2025★ 4 cited
Comet: Accelerating Private Inference for Large Language Model by Predicting Activation Sparsity
Guang Yan, Yuhui Zhang, Zimu Guo +6
With the growing use of large language models (LLMs) hosted on cloud platforms to offer inference services, privacy concerns about the potential leakage of sensitive information ar…