A principled framework for the design and analysis of token algorithms

30 May 2022

Papers citing "A principled framework for the design and analysis of token algorithms"

22 / 22 papers shown

Title
Walk for Learning: A Random Walk Approach for Federated Learning from Heterogeneous Data Ghadir Ayache Venkat Dassari S. E. Rouayheb FedML 35 19 0 01 Jun 2022
Asynchronous Parallel Incremental Block-Coordinate Descent for Decentralized Machine Learning Hao Chen Yu Ye Ming Xiao Mikael Skoglund 65 3 0 07 Feb 2022
Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks D. Kovalev Elnur Gasanov Peter Richtárik Alexander Gasnikov 46 44 0 08 Jun 2021
Privacy Amplification by Decentralization Edwige Cyffers A. Bellet FedML 92 41 0 09 Dec 2020
Dual-Free Stochastic Decentralized Optimization with Variance Reduction Hadrien Hendrikx Francis R. Bach Laurent Massoulié 37 25 0 25 Jun 2020
Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization D. Kovalev Adil Salim Peter Richtárik 36 83 0 21 Jun 2020
An Optimal Algorithm for Decentralized Finite Sum Optimization Hadrien Hendrikx Francis R. Bach Laurent Massoulie 52 45 0 20 May 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 78 505 0 23 Mar 2020
Revisiting EXTRA for Smooth Distributed Optimization Huan Li Zhouchen Lin 26 40 0 24 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 212 6,229 0 10 Dec 2019
An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums Hadrien Hendrikx Francis R. Bach Laurent Massoulie 62 31 0 27 May 2019
Accelerated Decentralized Optimization with Local Updates for Smooth and Strongly Convex Objectives Hadrien Hendrikx Francis R. Bach Laurent Massoulié 65 42 0 05 Oct 2018
A Dual Approach for Optimal Algorithms in Distributed Optimization over Networks César A. Uribe Soomin Lee Alexander Gasnikov A. Nedić 46 137 0 03 Sep 2018
Don't Use Large Mini-Batches, Use Local SGD Tao R. Lin Sebastian U. Stich Kumar Kshitij Patel Martin Jaggi 111 433 0 22 Aug 2018
Local SGD Converges Fast and Communicates Little Sebastian U. Stich FedML 164 1,061 0 24 May 2018
Walkman: A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization Xianghui Mao Kun Yuan Yubin Hu Yuantao Gu Ali H. Sayed W. Yin 45 58 0 18 Apr 2018
Optimal algorithms for smooth and strongly convex distributed optimization in networks Kevin Scaman Francis R. Bach Sébastien Bubeck Y. Lee Laurent Massoulié 58 329 0 28 Feb 2017
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 380 17,437 0 17 Feb 2016
SDCA without Duality, Regularization, and Individual Convexity Shai Shalev-Shwartz 39 104 0 04 Feb 2016
An optimal randomized incremental gradient method Guanghui Lan Yi Zhou 124 220 0 08 Jul 2015
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives Aaron Defazio Francis R. Bach Simon Lacoste-Julien ODL 131 1,823 0 01 Jul 2014
Minimizing Finite Sums with the Stochastic Average Gradient Mark Schmidt Nicolas Le Roux Francis R. Bach 312 1,245 0 10 Sep 2013