most citedPEFT-as-an-Attack! Jailbreaking Language Models during Federated Parameter-Efficient Fine-Tuning

1 citations · 1 across the 2 of their papers we have counts for

collaborators

2 papers

cs.AR2025

Prefill vs. Decode Bottlenecks: SRAM-Frequency Tradeoffs and the Memory-Bandwidth Ceiling

Hannah Atmer, Yuan Yao, Thiemo Voigt +1

Energy consumption dictates the cost and environmental impact of deploying Large Language Models. This paper investigates the impact of on-chip SRAM size and operating frequency on…

cs.CR2024★ 1 cited

PEFT-as-an-Attack! Jailbreaking Language Models during Federated Parameter-Efficient Fine-Tuning

Shenghui Li, Edith C. -H. Ngai, Fanghua Ye +1

Federated Parameter-Efficient Fine-Tuning (FedPEFT) has emerged as a promising paradigm for privacy-preserving and efficient adaptation of Pre-trained Language Models (PLMs) in Fed…