Achieving Linear Speedup with Partial Worker Participation in Non-IID
Federated Learning

Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning

27 January 2021

Haibo Yang

Papers citing "Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning"

8 / 58 papers shown

Title
Context-Aware Online Client Selection for Hierarchical Federated Learning Zhe Qu Rui Duan Lixing Chen Jie Xu Zhuo Lu Yao-Hong Liu 26 61 0 02 Dec 2021
EF21 with Bells & Whistles: Practical Algorithmic Extensions of Modern Error Feedback Ilyas Fatkhullin Igor Sokolov Eduard A. Gorbunov Zhize Li Peter Richtárik 44 44 0 07 Oct 2021
A Field Guide to Federated Optimization Jianyu Wang Zachary B. Charles Zheng Xu Gauri Joshi H. B. McMahan ... Mi Zhang Tong Zhang Chunxiang Zheng Chen Zhu Wennan Zhu FedML 178 411 0 14 Jul 2021
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy Xinwei Zhang Xiangyi Chen Min-Fong Hong Zhiwei Steven Wu Jinfeng Yi FedML 22 90 0 25 Jun 2021
On Large-Cohort Training for Federated Learning Zachary B. Charles Zachary Garrett Zhouyuan Huo Sergei Shmulyian Virginia Smith FedML 21 112 0 15 Jun 2021
Fast Federated Learning in the Presence of Arbitrary Device Unavailability Xinran Gu Kaixuan Huang Jingzhao Zhang Longbo Huang FedML 22 94 0 08 Jun 2021
Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers Guojun Xiong Gang Yan Rahul Singh Jian Li 28 12 0 11 Feb 2021
Adaptive Federated Learning in Resource Constrained Edge Computing Systems Shiqiang Wang Tiffany Tuor Theodoros Salonidis K. Leung C. Makaya T. He Kevin S. Chan 144 1,685 0 14 Apr 2018