Closing the Gap between Client and Global Model Performance in Heterogeneous Federated Learning

7 November 2022

Papers citing "Closing the Gap between Client and Global Model Performance in Heterogeneous Federated Learning"

10 / 10 papers shown

Title
Federated Model Distillation with Noise-Free Differential Privacy Lichao Sun Lingjuan Lyu FedML 73 107 0 11 Sep 2020
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning Hong-You Chen Wei-Lun Chao FedML 56 261 0 04 Sep 2020
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization Jianyu Wang Qinghua Liu Hao Liang Gauri Joshi H. Vincent Poor MoMe FedML 55 1,332 0 15 Jul 2020
Think Locally, Act Globally: Federated Learning with Local and Global Representations Paul Pu Liang Terrance Liu Liu Ziyin Nicholas B. Allen Randy P. Auerbach David Brent Ruslan Salakhutdinov Louis-Philippe Morency FedML 110 560 0 06 Jan 2020
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer Hong Chang Virat Shejwalkar Reza Shokri Amir Houmansadr FedML 75 168 0 24 Dec 2019
FedMD: Heterogenous Federated Learning via Model Distillation Daliang Li Junpu Wang FedML 88 854 0 08 Oct 2019
Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification T. Hsu Qi Matthew Brown FedML 138 1,148 0 13 Sep 2019
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 823 11,899 0 09 Mar 2017
Learning without Forgetting Zhizhong Li Derek Hoiem CLL OOD SSL 292 4,402 0 29 Jun 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 334 7,984 0 23 May 2016