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

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cs.LG2025

On-the-Fly Adaptation to Quantization: Configuration-Aware LoRA for Efficient Fine-Tuning of Quantized LLMs

Rongguang Ye, Ming Tang, Edith C. H. Ngai

As increasingly large pre-trained models are released, deploying them on edge devices for privacy-preserving applications requires effective compression. Recent works combine quant…

cs.LG2024★ 1 cited

Synergizing Foundation Models and Federated Learning: A Survey

Shenghui Li, Fanghua Ye, Meng Fang +4

Over the past few years, the landscape of Artificial Intelligence (AI) has been reshaped by the emergence of Foundation Models (FMs). Pre-trained on massive datasets, these models…