Distributed Coordinate Descent Method for Learning with Big Data

8 October 2013

Papers citing "Distributed Coordinate Descent Method for Learning with Big Data"

50 / 104 papers shown

Title
Beyond adaptive gradient: Fast-Controlled Minibatch Algorithm for large-scale optimization Corrado Coppola Lorenzo Papa Irene Amerini L. Palagi ODL 79 0 0 24 Nov 2024
FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training Philip Zmushko Aleksandr Beznosikov Martin Takáč Samuel Horváth 44 0 0 12 Nov 2024
A Mirror Descent-Based Algorithm for Corruption-Tolerant Distributed Gradient Descent Shuche Wang Vincent Y. F. Tan FedML OOD 49 1 0 19 Jul 2024
High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates Fred Lu Ryan R. Curtin Edward Raff Francis Ferraro James Holt 23 1 0 08 Jul 2024
Learning Scalable Model Soup on a Single GPU: An Efficient Subspace Training Strategy Tao Li Weisen Jiang Fanghui Liu X. Huang James T. Kwok MoMe 63 1 0 04 Jul 2024
Optimizing the Optimal Weighted Average: Efficient Distributed Sparse Classification Fred Lu Ryan R. Curtin Edward Raff Francis Ferraro James Holt 26 0 0 03 Jun 2024
Accelerating Heterogeneous Federated Learning with Closed-form Classifiers Eros Fani Raffaello Camoriano Barbara Caputo Marco Ciccone 47 4 0 03 Jun 2024
Estimation Network Design framework for efficient distributed optimization M. Bianchi Sergio Grammatico 25 0 0 23 Apr 2024
Scalable High-Dimensional Multivariate Linear Regression for Feature-Distributed Data Shuo-chieh Huang R. Tsay 29 0 0 07 Jul 2023
Towards a Better Theoretical Understanding of Independent Subnetwork Training Egor Shulgin Peter Richtárik AI4CE 28 6 0 28 Jun 2023
Batches Stabilize the Minimum Norm Risk in High Dimensional Overparameterized Linear Regression Shahar Stein Ioushua Inbar Hasidim O. Shayevitz M. Feder 19 0 0 14 Jun 2023
Global-QSGD: Practical Floatless Quantization for Distributed Learning with Theoretical Guarantees Jihao Xin Marco Canini Peter Richtárik Samuel Horváth 36 2 0 29 May 2023
Federated Empirical Risk Minimization via Second-Order Method S. Bian Zhao Song Junze Yin FedML 38 8 0 27 May 2023
Cooperative Coevolution for Non-Separable Large-Scale Black-Box Optimization: Convergence Analyses and Distributed Accelerations Qiqi Duan Chang Shao Guochen Zhou Hao Yang Qi Zhao Yuhui Shi 25 4 0 11 Apr 2023
Laplacian-based Semi-Supervised Learning in Multilayer Hypergraphs by Coordinate Descent Sara Venturini Andrea Cristofari Francesco Rinaldi Francesco Tudisco 39 2 0 28 Jan 2023
FLECS-CGD: A Federated Learning Second-Order Framework via Compression and Sketching with Compressed Gradient Differences A. Agafonov Brahim Erraji Martin Takáč FedML 35 4 0 18 Oct 2022
Federated Coordinate Descent for Privacy-Preserving Multiparty Linear Regression Xinlin Leng Chenxu Li Weifeng Xu Yuyan Sun Hongtao Wang FedML 32 1 0 16 Sep 2022
Flexible Vertical Federated Learning with Heterogeneous Parties Timothy Castiglia Shiqiang Wang S. Patterson FedML 34 34 0 26 Aug 2022
Assisted Learning for Organizations with Limited Imbalanced Data Cheng Chen Jiaying Zhou Jie Ding Yi Zhou FedML 13 3 0 20 Sep 2021
Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioning Anirban Das Timothy Castiglia Shiqiang Wang S. Patterson FedML 13 19 0 19 Aug 2021
Stability and Generalization for Randomized Coordinate Descent Puyu Wang Liang Wu Yunwen Lei 27 7 0 17 Aug 2021
Revisiting the Primal-Dual Method of Multipliers for Optimisation over Centralised Networks Guoqiang Zhang Kenta Niwa W. Kleijn 16 5 0 19 Jul 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 187 412 0 14 Jul 2021
Cross-Gradient Aggregation for Decentralized Learning from Non-IID data Yasaman Esfandiari Sin Yong Tan Zhanhong Jiang Aditya Balu Ethan Herron C. Hegde S. Sarkar OOD 22 50 0 02 Mar 2021
DONE: Distributed Approximate Newton-type Method for Federated Edge Learning Canh T. Dinh N. H. Tran Tuan Dung Nguyen Wei Bao A. R. Balef B. Zhou Albert Y. Zomaya FedML 23 15 0 10 Dec 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 34 0 0 26 Aug 2020
A Survey on Large-scale Machine Learning Meng Wang Weijie Fu Xiangnan He Shijie Hao Xindong Wu 19 109 0 10 Aug 2020
Fast-Convergent Federated Learning Hung T. Nguyen Vikash Sehwag Seyyedali Hosseinalipour Christopher G. Brinton M. Chiang H. Vincent Poor FedML 26 192 0 26 Jul 2020
Multi-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog Networks Seyyedali Hosseinalipour Sheikh Shams Azam Christopher G. Brinton Nicolò Michelusi Vaneet Aggarwal David J. Love H. Dai 21 91 0 18 Jul 2020
FedSplit: An algorithmic framework for fast federated optimization Reese Pathak Martin J. Wainwright FedML 42 182 0 11 May 2020
On the Convergence Analysis of Asynchronous SGD for Solving Consistent Linear Systems Atal Narayan Sahu Aritra Dutta Aashutosh Tiwari Peter Richtárik 13 5 0 05 Apr 2020
Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization Sudeep Salgia Qing Zhao Sattar Vakili 48 2 0 11 Mar 2020
A Survey on Distributed Machine Learning Joost Verbraeken Matthijs Wolting J. Katzy Jeroen Kloppenburg Tim Verbelen Jan S. Rellermeyer OOD 33 689 0 20 Dec 2019
SySCD: A System-Aware Parallel Coordinate Descent Algorithm Nikolas Ioannou Celestine Mendler-Dünner Thomas Parnell 15 3 0 18 Nov 2019
$DC^2$ : A Divide-and-conquer Algorithm for Large-scale Kernel Learning with Application to Clustering Ke Alexander Wang Xinran Bian Pan Liu Donghui Yan 21 3 0 16 Nov 2019
Privacy-Preserving Generalized Linear Models using Distributed Block Coordinate Descent E. V. Kesteren Chang Sun Daniel L. Oberski Michel Dumontier Lianne Ippel FedML 14 3 0 08 Nov 2019
Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling Mojmír Mutný Michal Derezinski Andreas Krause 35 20 0 25 Oct 2019
Machine Learning Systems for Highly-Distributed and Rapidly-Growing Data Kevin Hsieh SyDa OOD 14 4 0 18 Oct 2019
Decentralized Markov Chain Gradient Descent Tao Sun Dongsheng Li BDL 13 11 0 23 Sep 2019
From Server-Based to Client-Based Machine Learning: A Comprehensive Survey Renjie Gu Chaoyue Niu Fan Wu Guihai Chen Chun Hu Chengfei Lyu Zhihua Wu 27 25 0 18 Sep 2019
Federated Learning: Challenges, Methods, and Future Directions Tian Li Anit Kumar Sahu Ameet Talwalkar Virginia Smith FedML 54 4,417 0 21 Aug 2019
On Convergence of Distributed Approximate Newton Methods: Globalization, Sharper Bounds and Beyond Xiao-Tong Yuan Ping Li 16 32 0 06 Aug 2019
Learning over inherently distributed data Donghui Yan Ying Xu FedML 13 2 0 30 Jul 2019
Decentralized Deep Learning with Arbitrary Communication Compression Anastasia Koloskova Tao R. Lin Sebastian U. Stich Martin Jaggi FedML 28 233 0 22 Jul 2019
On the Convergence of FedAvg on Non-IID Data Xiang Li Kaixuan Huang Wenhao Yang Shusen Wang Zhihua Zhang FedML 70 2,283 0 04 Jul 2019
The Communication Complexity of Optimization Santosh Vempala Ruosong Wang David P. Woodruff 11 32 0 13 Jun 2019
Communication-Efficient Accurate Statistical Estimation Jianqing Fan Yongyi Guo Kaizheng Wang 19 110 0 12 Jun 2019
Natural Compression for Distributed Deep Learning Samuel Horváth Chen-Yu Ho L. Horvath Atal Narayan Sahu Marco Canini Peter Richtárik 21 151 0 27 May 2019
Distributed Byzantine Tolerant Stochastic Gradient Descent in the Era of Big Data Richeng Jin Xiaofan He H. Dai FedML 16 13 0 27 Feb 2019
Distributed Learning with Compressed Gradient Differences Konstantin Mishchenko Eduard A. Gorbunov Martin Takáč Peter Richtárik 15 197 0 26 Jan 2019