CELLM: An Efficient Communication in Large Language Models Training for Federated Learning

30 July 2024

Papers citing "CELLM: An Efficient Communication in Large Language Models Training for Federated Learning"

9 / 9 papers shown

Title
Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers Libo Qin Qiguang Chen Yuhang Zhou Zhi Chen Yinghui Li Lizi Liao Min Li Wanxiang Che Philip S. Yu LRM 47 36 0 07 Apr 2024
Adaptive Federated Pruning in Hierarchical Wireless Networks Xiaonan Liu Shiqiang Wang Yansha Deng A. Nallanathan 24 11 0 15 May 2023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy Natalia Ponomareva Hussein Hazimeh Alexey Kurakin Zheng Xu Carson E. Denison H. B. McMahan Sergei Vassilvitskii Steve Chien Abhradeep Thakurta 94 167 0 01 Mar 2023
Conquering the Communication Constraints to Enable Large Pre-Trained Models in Federated Learning Guangyu Sun Umar Khalid Matías Mendieta Taojiannan Yang C. L. P. Chen FedML 67 13 0 04 Oct 2022
Papaya: Practical, Private, and Scalable Federated Learning Dzmitry Huba John Nguyen Kshitiz Malik Ruiyu Zhu Michael G. Rabbat ... H. Srinivas Kaikai Wang Anthony Shoumikhin Jesik Min Mani Malek FedML 99 135 0 08 Nov 2021
The Power of Scale for Parameter-Efficient Prompt Tuning Brian Lester Rami Al-Rfou Noah Constant VPVLM 280 3,835 0 18 Apr 2021
Towards Personalized Federated Learning A. Tan Han Yu Li-zhen Cui Qiang Yang FedML AI4CE 183 840 0 01 Mar 2021
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 226 4,424 0 23 Jan 2020
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization Amirhossein Reisizadeh Aryan Mokhtari Hamed Hassani Ali Jadbabaie Ramtin Pedarsani FedML 157 758 0 28 Sep 2019