CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large
Language Models

CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models

22 May 2024

Huiwen Wu

Papers citing "CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models"

10 / 10 papers shown

Title
On the Effectiveness of Parameter-Efficient Fine-Tuning Z. Fu Haoran Yang Anthony Man-Cho So Wai Lam Lidong Bing Nigel Collier 49 156 0 28 Nov 2022
LoRA: Low-Rank Adaptation of Large Language Models J. E. Hu Yelong Shen Phillip Wallis Zeyuan Allen-Zhu Yuanzhi Li Shean Wang Lu Wang Weizhu Chen OffRL AI4TS AI4CE ALM AIMat 304 10,099 0 17 Jun 2021
GLM: General Language Model Pretraining with Autoregressive Blank Infilling Zhengxiao Du Yujie Qian Xiao Liu Ming Ding J. Qiu Zhilin Yang Jie Tang BDL AI4CE 100 1,520 0 18 Mar 2021
Learned Gradient Compression for Distributed Deep Learning L. Abrahamyan Yiming Chen Giannis Bekoulis Nikos Deligiannis 62 46 0 16 Mar 2021
Federated Learning: Opportunities and Challenges P. Mammen FedML 84 220 0 14 Jan 2021
UVeQFed: Universal Vector Quantization for Federated Learning Nir Shlezinger Mingzhe Chen Yonina C. Eldar H. Vincent Poor Shuguang Cui FedML MQ 49 225 0 05 Jun 2020
Inverting Gradients -- How easy is it to break privacy in federated learning? Jonas Geiping Hartmut Bauermeister Hannah Dröge Michael Moeller FedML 82 1,217 0 31 Mar 2020
Federated Learning with Differential Privacy: Algorithms and Performance Analysis Kang Wei Jun Li Ming Ding Chuan Ma Heng Yang Farokhi Farhad Shi Jin Tony Q.S. Quek H. Vincent Poor FedML 97 1,589 0 01 Nov 2019
Federated Learning: Strategies for Improving Communication Efficiency Jakub Konecný H. B. McMahan Felix X. Yu Peter Richtárik A. Suresh Dave Bacon FedML 271 4,620 0 18 Oct 2016
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 251 17,328 0 17 Feb 2016