On the effectiveness of partial variance reduction in federated learning with heterogeneous data

5 December 2022

Papers citing "On the effectiveness of partial variance reduction in federated learning with heterogeneous data"

35 / 35 papers shown

Title
Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning Yanbiao Ma Wei-Ming Dai Wenke Huang Jiayi Chen 154 0 0 09 Mar 2025
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels Yaodong Yu Alexander Wei Sai Praneeth Karimireddy Yi-An Ma Michael I. Jordan FedML 41 30 0 13 Jul 2022
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning Liam Collins Hamed Hassani Aryan Mokhtari Sanjay Shakkottai FedML 54 77 0 27 May 2022
AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation Farshid Varno Marzie Saghayi Laya Rafiee Sharut Gupta Stan Matwin Mohammad Havaei FedML 94 30 0 27 Apr 2022
FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction Liang Gao Huazhu Fu Li Li Yingwen Chen Minghua Xu Chengzhong Xu FedML 61 248 0 22 Mar 2022
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! Konstantin Mishchenko Grigory Malinovsky Sebastian U. Stich Peter Richtárik 21 154 0 18 Feb 2022
Federated Learning Based on Dynamic Regularization D. A. E. Acar Yue Zhao Ramon Matas Navarro Matthew Mattina P. Whatmough Venkatesh Saligrama FedML 37 762 0 08 Nov 2021
RelaySum for Decentralized Deep Learning on Heterogeneous Data Thijs Vogels Lie He Anastasia Koloskova Tao R. Lin Sai Praneeth Karimireddy Sebastian U. Stich Martin Jaggi FedML MoE 31 61 0 08 Oct 2021
FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning Haoyu Zhao Zhize Li Peter Richtárik FedML 30 29 0 10 Aug 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 244 415 0 14 Jul 2021
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data Mi Luo Fei Chen Dapeng Hu Yifan Zhang Jian Liang Jiashi Feng FedML 60 332 0 09 Jun 2021
FedBABU: Towards Enhanced Representation for Federated Image Classification Jaehoon Oh Sangmook Kim Se-Young Yun FedML 45 202 0 04 Jun 2021
Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth Thao Nguyen M. Raghu Simon Kornblith OOD 26 271 0 29 Oct 2020
Uncertainty Sets for Image Classifiers using Conformal Prediction Anastasios Nikolas Angelopoulos Stephen Bates Jitendra Malik Michael I. Jordan UQCV 117 326 0 29 Sep 2020
Ensemble Distillation for Robust Model Fusion in Federated Learning Tao R. Lin Lingjing Kong Sebastian U. Stich Martin Jaggi FedML 47 1,026 0 12 Jun 2020
Classification with Valid and Adaptive Coverage Yaniv Romano Matteo Sesia Emmanuel J. Candès 148 315 0 03 Jun 2020
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates Anastasia Koloskova Nicolas Loizou Sadra Boreiri Martin Jaggi Sebastian U. Stich FedML 61 499 0 23 Mar 2020
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization Zhize Li D. Kovalev Xun Qian Peter Richtárik FedML AI4CE 71 136 0 26 Feb 2020
A Simple Framework for Contrastive Learning of Visual Representations Ting-Li Chen Simon Kornblith Mohammad Norouzi Geoffrey E. Hinton SSL 158 18,523 0 13 Feb 2020
Advances and Open Problems in Federated Learning Peter Kairouz H. B. McMahan Brendan Avent A. Bellet M. Bennis ... Zheng Xu Qiang Yang Felix X. Yu Han Yu Sen Zhao FedML AI4CE 98 6,177 0 10 Dec 2019
On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning Aritra Dutta El Houcine Bergou A. Abdelmoniem Chen-Yu Ho Atal Narayan Sahu Marco Canini Panos Kalnis 40 77 0 19 Nov 2019
The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Communication Sebastian U. Stich Sai Praneeth Karimireddy FedML 40 20 0 11 Sep 2019
Unified Optimal Analysis of the (Stochastic) Gradient Method Sebastian U. Stich 48 113 0 09 Jul 2019
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent Eduard A. Gorbunov Filip Hanzely Peter Richtárik 75 143 0 27 May 2019
Similarity of Neural Network Representations Revisited Simon Kornblith Mohammad Norouzi Honglak Lee Geoffrey E. Hinton 113 1,382 0 01 May 2019
Distributed Learning with Compressed Gradient Differences Konstantin Mishchenko Eduard A. Gorbunov Martin Takáč Peter Richtárik 74 197 0 26 Jan 2019
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning Aaron Defazio Léon Bottou UQCV DRL 38 112 0 11 Dec 2018
Federated Optimization: Distributed Machine Learning for On-Device Intelligence Jakub Konecný H. B. McMahan Daniel Ramage Peter Richtárik FedML 91 1,886 0 08 Oct 2016
Densely Connected Convolutional Networks Gao Huang Zhuang Liu Laurens van der Maaten Kilian Q. Weinberger PINN 3DV 580 36,599 0 25 Aug 2016
SGDR: Stochastic Gradient Descent with Warm Restarts I. Loshchilov Frank Hutter ODL 203 8,030 0 13 Aug 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 1.1K 192,638 0 10 Dec 2015
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 751 99,991 0 04 Sep 2014
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives Aaron Defazio Francis R. Bach Simon Lacoste-Julien ODL 93 1,817 0 01 Jul 2014
Communication Efficient Distributed Optimization using an Approximate Newton-type Method Ohad Shamir Nathan Srebro Tong Zhang 48 554 0 30 Dec 2013
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 140 14,831 1 21 Dec 2013