FedGPO: Heterogeneity-Aware Global Parameter Optimization for Efficient Federated Learning

30 November 2022

Papers citing "FedGPO: Heterogeneity-Aware Global Parameter Optimization for Efficient Federated Learning"

7 / 7 papers shown

Title
Training Machine Learning models at the Edge: A Survey Aymen Rayane Khouas Mohamed Reda Bouadjenek Hakim Hacid Sunil Aryal 24 9 0 05 Mar 2024
Exploring and Exploiting Data Heterogeneity in Recommendation Zimu Wang Jiashuo Liu Hao Zou Xingxuan Zhang Yue He Dongxu Liang Peng Cui 19 2 0 21 May 2023
Green Federated Learning Ashkan Yousefpour Sheng Guo Ashish Shenoy Sayan Ghosh Pierre Stock Kiwan Maeng Schalk-Willem Kruger Michael G. Rabbat Carole-Jean Wu Ilya Mironov FedML AI4CE 26 10 0 26 Mar 2023
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 97 133 0 08 Nov 2021
FedProc: Prototypical Contrastive Federated Learning on Non-IID data Xutong Mu Yulong Shen Ke Cheng Xueli Geng Jiaxuan Fu Tao Zhang Zhiwei Zhang FedML 33 161 0 25 Sep 2021
Federated Learning on Non-IID Data Silos: An Experimental Study Q. Li Yiqun Diao Quan Chen Bingsheng He FedML OOD 85 936 0 03 Feb 2021
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand M. Andreetto Hartwig Adam 3DH 948 20,214 0 17 Apr 2017