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Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity

23 June 2023

Papers citing "Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity"

30 / 30 papers shown

Title
On the effectiveness of partial variance reduction in federated learning with heterogeneous data Yue Liu Mikkel N. Schmidt T. S. Alstrøm Sebastian U. Stich FedML 72 9 0 05 Dec 2022
Federated Learning with GAN-based Data Synthesis for Non-IID Clients Zijian Li Jiawei Shao Yuyi Mao Jessie Hui Wang Jinchao Zhang FedML 82 40 0 11 Jun 2022
On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data Jianyu Wang Rudrajit Das Gauri Joshi Satyen Kale Zheng Xu Tong Zhang FedML 82 40 0 09 Jun 2022
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning Liam Collins Hamed Hassani Aryan Mokhtari Sanjay Shakkottai FedML 70 78 0 27 May 2022
Federated Learning Based on Dynamic Regularization D. A. E. Acar Yue Zhao Ramon Matas Navarro Matthew Mattina P. Whatmough Venkatesh Saligrama FedML 71 776 0 08 Nov 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 273 419 0 14 Jul 2021
FedMix: Approximation of Mixup under Mean Augmented Federated Learning Tehrim Yoon Sumin Shin Sung Ju Hwang Eunho Yang FedML 68 171 0 01 Jul 2021
On Large-Cohort Training for Federated Learning Zachary B. Charles Zachary Garrett Zhouyuan Huo Sergei Shmulyian Virginia Smith FedML 66 112 0 15 Jun 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 83 340 0 09 Jun 2021
Data-Free Knowledge Distillation for Heterogeneous Federated Learning Zhuangdi Zhu Junyuan Hong Jiayu Zhou FedML 80 659 0 20 May 2021
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness? Vikash Sehwag Saeed Mahloujifar Tinashe Handina Sihui Dai Chong Xiang M. Chiang Prateek Mittal OOD 85 130 0 19 Apr 2021
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients A. Mitra Rayana H. Jaafar George J. Pappas Hamed Hassani FedML 101 159 0 14 Feb 2021
Negative Data Augmentation Abhishek Sinha Kumar Ayush Jiaming Song Burak Uzkent Hongxia Jin Stefano Ermon 78 75 0 09 Feb 2021
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning Sai Praneeth Karimireddy Martin Jaggi Satyen Kale M. Mohri Sashank J. Reddi Sebastian U. Stich A. Suresh FedML 133 217 0 08 Aug 2020
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization Jianyu Wang Qinghua Liu Hao Liang Gauri Joshi H. Vincent Poor MoMe FedML 68 1,337 0 15 Jul 2020
Differentiable Augmentation for Data-Efficient GAN Training Shengyu Zhao Zhijian Liu Ji Lin Jun-Yan Zhu Song Han 91 608 0 18 Jun 2020
GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators Dingfan Chen Tribhuvanesh Orekondy Mario Fritz SyDa 56 185 0 15 Jun 2020
Training Generative Adversarial Networks with Limited Data Tero Karras M. Aittala Janne Hellsten S. Laine J. Lehtinen Timo Aila GAN 159 1,886 0 11 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 85 506 0 23 Mar 2020
Adaptive Federated Optimization Sashank J. Reddi Zachary B. Charles Manzil Zaheer Zachary Garrett Keith Rush Jakub Konecný Sanjiv Kumar H. B. McMahan FedML 177 1,437 0 29 Feb 2020
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization Zhize Li D. Kovalev Xun Qian Peter Richtárik FedML AI4CE 104 137 0 26 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 259 6,261 0 10 Dec 2019
Tighter Theory for Local SGD on Identical and Heterogeneous Data Ahmed Khaled Konstantin Mishchenko Peter Richtárik 74 433 0 10 Sep 2019
Unified Optimal Analysis of the (Stochastic) Gradient Method Sebastian U. Stich 54 113 0 09 Jul 2019
Differentially Private Generative Adversarial Network Liyang Xie Kaixiang Lin Shu Wang Fei Wang Jiayu Zhou SyDa 90 500 0 19 Feb 2018
Learning from Simulated and Unsupervised Images through Adversarial Training A. Shrivastava Tomas Pfister Oncel Tuzel J. Susskind Wenda Wang Russ Webb GAN 103 1,801 0 22 Dec 2016
Federated Optimization: Distributed Machine Learning for On-Device Intelligence Jakub Konecný H. B. McMahan Daniel Ramage Peter Richtárik FedML 138 1,899 0 08 Oct 2016
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 216 6,130 0 01 Jul 2016
Improved Techniques for Training GANs Tim Salimans Ian Goodfellow Wojciech Zaremba Vicki Cheung Alec Radford Xi Chen GAN 483 9,052 0 10 Jun 2016
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 406 17,486 0 17 Feb 2016