Efficient and Light-Weight Federated Learning via Asynchronous
Distributed Dropout

Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout

28 October 2022

Chen Dun

Mirian Hipolito Garcia

Dimitrios Dimitriadis

Anastasios Kyrillidis

Papers citing "Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout"

7 / 7 papers shown

Title
Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis Guangchen Lan Dong-Jun Han Abolfazl Hashemi Vaneet Aggarwal Christopher G. Brinton 104 15 0 09 Apr 2024
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
Asynchronous Federated Learning on Heterogeneous Devices: A Survey Chenhao Xu Youyang Qu Yong Xiang Longxiang Gao FedML 83 162 0 09 Sep 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 160 358 0 14 Jul 2021
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout Samuel Horváth Stefanos Laskaridis Mario Almeida Ilias Leondiadis Stylianos I. Venieris Nicholas D. Lane 157 206 0 26 Feb 2021
Stragglers Are Not Disaster: A Hybrid Federated Learning Algorithm with Delayed Gradients Xingyu Li Zhe Qu Bo Tang Zhuo Lu FedML 51 31 0 12 Feb 2021
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 243 8,157 0 06 Jun 2015