Efficient and Private Federated Learning with Partially Trainable Networks

6 October 2021

Papers citing "Efficient and Private Federated Learning with Partially Trainable Networks"

6 / 6 papers shown

Title
Maestro: Uncovering Low-Rank Structures via Trainable Decomposition Samuel Horváth Stefanos Laskaridis Shashank Rajput Hongyi Wang BDL 24 4 0 28 Aug 2023
TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Training Tuo Zhang Lei Gao Sunwoo Lee Mi Zhang Salman Avestimehr FedML 14 28 0 14 Apr 2023
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 167 410 0 14 Jul 2021
Practical and Private (Deep) Learning without Sampling or Shuffling Peter Kairouz Brendan McMahan Shuang Song Om Thakkar Abhradeep Thakurta Zheng Xu FedML 170 193 0 26 Feb 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 168 267 0 26 Feb 2021
Echo State Speech Recognition H. Shrivastava Ankush Garg Yuan Cao Yu Zhang Tara N. Sainath 29 22 0 18 Feb 2021