FATE-LLM: A Industrial Grade Federated Learning Framework for Large
Language Models

FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models

16 October 2023

Papers citing "FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models"

9 / 9 papers shown

Title
Enhancing Noisy Functional Encryption for Privacy-Preserving Machine Learning Linda Scheu-Hachtel Jasmin Zalonis 34 0 0 09 May 2025
Decentralized Low-Rank Fine-Tuning of Large Language Models Sajjad Ghiasvand Mahnoosh Alizadeh Ramtin Pedarsani ALM 64 0 0 26 Jan 2025
Data Quality Control in Federated Instruction-tuning of Large Language Models Yaxin Du Rui Ye Fengting Yuchi W. Zhao Jingjing Qu Y. Wang Siheng Chen ALM FedML 45 0 0 15 Oct 2024
Mobile Edge Intelligence for Large Language Models: A Contemporary Survey Guanqiao Qu Qiyuan Chen Wei Wei Zheng Lin Xianhao Chen Kaibin Huang 40 43 0 09 Jul 2024
Recent Advances in Federated Learning Driven Large Language Models: A Survey on Architecture, Performance, and Security Youyang Qu Ming Liu Tianqing Zhu Longxiang Gao Shui Yu Wanlei Zhou MU FedML 54 2 0 14 Jun 2024
Horizontal Federated Computer Vision Paul K. Mandal Cole Leo Connor Hurley FedML ObjD 33 0 0 31 Dec 2023
Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes Zhen Qin Daoyuan Chen Bingchen Qian Bolin Ding Yaliang Li Shuiguang Deng FedML 32 32 0 11 Dec 2023
Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond Jingfeng Yang Hongye Jin Ruixiang Tang Xiaotian Han Qizhang Feng Haoming Jiang Bing Yin Xia Hu LM&MA 125 619 0 26 Apr 2023
P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks Xiao Liu Kaixuan Ji Yicheng Fu Weng Lam Tam Zhengxiao Du Zhilin Yang Jie Tang VLM 236 805 0 14 Oct 2021