Combating Client Dropout in Federated Learning via Friend Model Substitution

26 May 2022

Papers citing "Combating Client Dropout in Federated Learning via Friend Model Substitution"

15 / 15 papers shown

Title
FedSoft: Soft Clustered Federated Learning with Proximal Local Updating Yichen Ruan Carlee Joe-Wong FedML 50 94 0 11 Dec 2021
Anarchic Federated Learning Haibo Yang Xin Zhang Prashant Khanduri Jia Liu FedML 36 58 0 23 Aug 2021
Node Selection Toward Faster Convergence for Federated Learning on Non-IID Data Hongda Wu Ping Wang FedML 65 139 0 14 May 2021
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning Haibo Yang Minghong Fang Jia Liu FedML 62 258 0 27 Jan 2021
Optimal Client Sampling for Federated Learning Jiajun He Samuel Horváth Peter Richtárik FedML 75 200 0 26 Oct 2020
An Efficient Framework for Clustered Federated Learning Avishek Ghosh Jichan Chung Dong Yin Kannan Ramchandran FedML 65 857 0 07 Jun 2020
Adaptive Federated Optimization Sashank J. Reddi Zachary B. Charles Manzil Zaheer Zachary Garrett Keith Rush Jakub Konecný Sanjiv Kumar H. B. McMahan FedML 163 1,431 0 29 Feb 2020
Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability Yikai Yan Chaoyue Niu Yucheng Ding Zhenzhe Zheng Fan Wu Guihai Chen Shaojie Tang Zhihua Wu FedML 140 38 0 18 Feb 2020
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning Sai Praneeth Karimireddy Satyen Kale M. Mohri Sashank J. Reddi Sebastian U. Stich A. Suresh FedML 65 346 0 14 Oct 2019
SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead A. Masullo Ligang He Toby Perrett Rui Mao Carsten Maple Majid Mirmehdi 66 313 0 03 Oct 2019
Robust and Communication-Efficient Federated Learning from Non-IID Data Felix Sattler Simon Wiedemann K. Müller Wojciech Samek FedML 64 1,353 0 07 Mar 2019
Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms Jianyu Wang Gauri Joshi 154 349 0 22 Aug 2018
Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning Hao Yu Sen Yang Shenghuo Zhu MoMe FedML 73 604 0 17 Jul 2018
Local SGD Converges Fast and Communicates Little Sebastian U. Stich FedML 164 1,061 0 24 May 2018
Federated Learning: Strategies for Improving Communication Efficiency Jakub Konecný H. B. McMahan Felix X. Yu Peter Richtárik A. Suresh Dave Bacon FedML 291 4,636 0 18 Oct 2016