Faster Adaptive Momentum-Based Federated Methods for Distributed Composition Optimization

3 November 2022

Papers citing "Faster Adaptive Momentum-Based Federated Methods for Distributed Composition Optimization"

38 / 38 papers shown

Title
Stochastic Controlled Averaging for Federated Learning with Communication Compression Xinmeng Huang Ping Li Xiaoyun Li 93 207 0 16 Aug 2023
FedNest: Federated Bilevel, Minimax, and Compositional Optimization Davoud Ataee Tarzanagh Mingchen Li Christos Thrampoulidis Samet Oymak FedML 79 74 0 04 May 2022
Optimal Algorithms for Stochastic Multi-Level Compositional Optimization Wei Jiang Bokun Wang Yibo Wang Lijun Zhang Tianbao Yang 122 18 0 15 Feb 2022
Toward Communication Efficient Adaptive Gradient Method Xiangyi Chen Xiaoyun Li P. Li FedML 59 42 0 10 Sep 2021
Compositional federated learning: Applications in distributionally robust averaging and meta learning Feihu Huang Junyi Li FedML 64 15 0 21 Jun 2021
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning Prashant Khanduri Pranay Sharma Haibo Yang Min-Fong Hong Jia Liu K. Rajawat P. Varshney FedML 50 63 0 19 Jun 2021
SUPER-ADAM: Faster and Universal Framework of Adaptive Gradients Feihu Huang Junyi Li Heng-Chiao Huang ODL 50 42 0 15 Jun 2021
Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiency Yuyang Deng M. Mahdavi 86 61 0 25 Feb 2021
Distributionally Robust Federated Averaging Yuyang Deng Mohammad Mahdi Kamani M. Mahdavi FedML 45 142 0 25 Feb 2021
FLOP: Federated Learning on Medical Datasets using Partial Networks Qiang Yang Jianyi Zhang Weituo Hao Gregory P. Spell Lawrence Carin FedML OOD 53 87 0 10 Feb 2021
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling Cheng Chen Ziyi Chen Yi Zhou B. Kailkhura FedML 61 61 0 22 Sep 2020
Solving Stochastic Compositional Optimization is Nearly as Easy as Solving Stochastic Optimization Tianyi Chen Yuejiao Sun W. Yin 103 82 0 25 Aug 2020
Federated Accelerated Stochastic Gradient Descent Honglin Yuan Tengyu Ma FedML 64 180 0 16 Jun 2020
Robust Federated Learning: The Case of Affine Distribution Shifts Amirhossein Reisizadeh Farzan Farnia Ramtin Pedarsani Ali Jadbabaie FedML OOD 82 164 0 16 Jun 2020
Adaptive Personalized Federated Learning Yuyang Deng Mohammad Mahdi Kamani M. Mahdavi FedML 301 556 0 30 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
Personalized Federated Learning: A Meta-Learning Approach Alireza Fallah Aryan Mokhtari Asuman Ozdaglar FedML 154 569 0 19 Feb 2020
Compositional ADAM: An Adaptive Compositional Solver Rasul Tutunov Minne Li Alexander I. Cowen-Rivers Jun Wang Haitham Bou-Ammar ODL 89 16 0 10 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 256 6,261 0 10 Dec 2019
Multi-Level Composite Stochastic Optimization via Nested Variance Reduction Junyu Zhang Lin Xiao 109 56 0 29 Aug 2019
A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization Quoc Tran-Dinh Nhan H. Pham T. Dzung Lam M. Nguyen 60 50 0 08 Jul 2019
On the Convergence of FedAvg on Non-IID Data Xiang Li Kaixuan Huang Wenhao Yang Shusen Wang Zhihua Zhang FedML 140 2,334 0 04 Jul 2019
Momentum-Based Variance Reduction in Non-Convex SGD Ashok Cutkosky Francesco Orabona ODL 84 407 0 24 May 2019
On the Convergence of Adam and Beyond Sashank J. Reddi Satyen Kale Surinder Kumar 93 2,499 0 19 Apr 2019
Agnostic Federated Learning M. Mohri Gary Sivek A. Suresh FedML 129 935 0 01 Feb 2019
Error Feedback Fixes SignSGD and other Gradient Compression Schemes Sai Praneeth Karimireddy Quentin Rebjock Sebastian U. Stich Martin Jaggi 66 502 0 28 Jan 2019
On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization Xiangyi Chen Sijia Liu Ruoyu Sun Mingyi Hong 55 323 0 08 Aug 2018
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator Cong Fang C. J. Li Zhouchen Lin Tong Zhang 89 579 0 04 Jul 2018
Improved Sample Complexity for Stochastic Compositional Variance Reduced Gradient Tianyi Lin Chenyou Fan Mengdi Wang Michael I. Jordan 56 24 0 01 Jun 2018
Local SGD Converges Fast and Communicates Little Sebastian U. Stich FedML 170 1,063 0 24 May 2018
Accelerated Method for Stochastic Composition Optimization with Nonsmooth Regularization Zhouyuan Huo Bin Gu Ji Liu Heng-Chiao Huang 87 51 0 10 Nov 2017
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 823 11,909 0 09 Mar 2017
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient Lam M. Nguyen Jie Liu K. Scheinberg Martin Takáč ODL 162 606 0 01 Mar 2017
Accelerating Stochastic Composition Optimization Mengdi Wang Ji Liu Ethan X. Fang 56 148 0 25 Jul 2016
Learning to learn by gradient descent by gradient descent Marcin Andrychowicz Misha Denil Sergio Gomez Colmenarejo Matthew W. Hoffman David Pfau Tom Schaul Brendan Shillingford Nando de Freitas 108 2,006 0 14 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
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 1.8K 150,115 0 22 Dec 2014
Stochastic Compositional Gradient Descent: Algorithms for Minimizing Compositions of Expected-Value Functions Mengdi Wang Ethan X. Fang Han Liu 96 264 0 14 Nov 2014