Tunable Soft Prompts are Messengers in Federated Learning

12 November 2023

Papers citing "Tunable Soft Prompts are Messengers in Federated Learning"

5 / 5 papers shown

Title
Mitigating Group-Level Fairness Disparities in Federated Visual Language Models Chaomeng Chen Zitong Yu J. Dong Sen Su L. Shen Shutao Xia Xiaochun Cao FedML VLM 67 0 0 03 May 2025
Communication-Efficient and Personalized Federated Foundation Model Fine-Tuning via Tri-Matrix Adaptation Y. Li Bo Liu Sheng Huang Z. Zhang Xiaotong Yuan Richang Hong 41 0 0 31 Mar 2025
Ten Challenging Problems in Federated Foundation Models Tao Fan Hanlin Gu Xuemei Cao Chee Seng Chan Qian Chen ... Y. Zhang Xiaojin Zhang Zhenzhe Zheng Lixin Fan Qiang Yang FedML 73 3 0 14 Feb 2025
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA Jianhao Zhu Changze Lv Xiaohua Wang Muling Wu Wenhao Liu Tianlong Li Zixuan Ling Cenyuan Zhang Xiaoqing Zheng Xuanjing Huang 29 2 0 16 Jun 2024
The Power of Scale for Parameter-Efficient Prompt Tuning Brian Lester Rami Al-Rfou Noah Constant VPVLM 278 3,784 0 18 Apr 2021