Local SGD Converges Fast and Communicates Little

24 May 2018

Papers citing "Local SGD Converges Fast and Communicates Little"

50 / 629 papers shown

Title
Heterogeneous Federated Learning on a Graph Huiyuan Wang Xuyang Zhao Weijie Lin FedML 59 4 0 19 Sep 2022
Personalized Federated Learning with Communication Compression El Houcine Bergou Konstantin Burlachenko Aritra Dutta Peter Richtárik FedML 80 9 0 12 Sep 2022
Federated Zero-Shot Learning for Visual Recognition Zhi Chen Yadan Luo Sen Wang Jingjing Li Zi Huang FedML 29 3 0 05 Sep 2022
HammingMesh: A Network Topology for Large-Scale Deep Learning Torsten Hoefler Tommaso Bonato Daniele De Sensi Salvatore Di Girolamo Shigang Li Marco Heddes Jon Belk Deepak Goel Miguel Castro Steve Scott 3DH GNN AI4CE 32 20 0 03 Sep 2022
Exact Penalty Method for Federated Learning Shenglong Zhou Geoffrey Ye Li FedML 33 0 0 23 Aug 2022
Joint Privacy Enhancement and Quantization in Federated Learning Natalie Lang Elad Sofer Tomer Shaked Nir Shlezinger FedML 39 46 0 23 Aug 2022
NET-FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data Xin Zhang Minghong Fang Zhuqing Liu Haibo Yang Jia-Wei Liu Zhengyuan Zhu FedML 27 14 0 17 Aug 2022
On the Convergence of Multi-Server Federated Learning with Overlapping Area Zhe Qu Xingyu Li Jie Xu Bo Tang Zhuo Lu Yao-Hong Liu FedML 50 15 0 16 Aug 2022
FedMR: Fedreated Learning via Model Recombination Ming Hu Zhihao Yue Zhiwei Ling Xian Wei Mingsong Chen FedML 21 0 0 16 Aug 2022
Adaptive Learning Rates for Faster Stochastic Gradient Methods Samuel Horváth Konstantin Mishchenko Peter Richtárik ODL 41 7 0 10 Aug 2022
Fast Heterogeneous Federated Learning with Hybrid Client Selection Guangyuan Shen D. Gao Duanxiao Song Libin Yang Xukai Zhou Shirui Pan W. Lou Fang Zhou FedML 45 12 0 10 Aug 2022
Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates Fangcheng Fu Xupeng Miao Jiawei Jiang Huanran Xue Bin Cui FedML 32 21 0 29 Jul 2022
FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning Divyansh Jhunjhunwala Pranay Sharma Aushim Nagarkatti Gauri Joshi FedML 52 42 0 28 Jul 2022
CFLIT: Coexisting Federated Learning and Information Transfer Zehong Lin Hang Liu Y. Zhang 17 11 0 26 Jul 2022
Fast Composite Optimization and Statistical Recovery in Federated Learning Yajie Bao M. Crawshaw Sha Luo Mingrui Liu FedML 42 16 0 17 Jul 2022
Multi-Model Federated Learning with Provable Guarantees Neelkamal Bhuyan Sharayu Moharir Gauri Joshi FedML 32 14 0 09 Jul 2022
Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with Inexact Prox Abdurakhmon Sadiev D. Kovalev Peter Richtárik 32 20 0 08 Jul 2022
Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced Topology Yan Huang Ying Sun Zehan Zhu Changzhi Yan Jinming Xu FedML 35 15 0 08 Jul 2022
FAIR-BFL: Flexible and Incentive Redesign for Blockchain-based Federated Learning Rongxin Xu Shiva Raj Pokhrel Qiujun Lan Gang Li 25 7 0 26 Jun 2022
On the Importance and Applicability of Pre-Training for Federated Learning Hong-You Chen Cheng-Hao Tu Zi-hua Li Hang Shen Wei-Lun Chao FedML 26 78 0 23 Jun 2022
A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates Yann Fraboni Richard Vidal Laetitia Kameni Marco Lorenzi FedML 27 24 0 21 Jun 2022
Communication-Efficient Federated Learning With Data and Client Heterogeneity Hossein Zakerinia Shayan Talaei Giorgi Nadiradze Dan Alistarh FedML 31 7 0 20 Jun 2022
Pisces: Efficient Federated Learning via Guided Asynchronous Training Zhifeng Jiang Wei Wang Baochun Li Bo-wen Li FedML 27 24 0 18 Jun 2022
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning Anis Elgabli Chaouki Ben Issaid Amrit Singh Bedi K. Rajawat M. Bennis Vaneet Aggarwal FedML 13 30 0 17 Jun 2022
Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning Anastasia Koloskova Sebastian U. Stich Martin Jaggi FedML 30 79 0 16 Jun 2022
Federated Data Analytics: A Study on Linear Models Xubo Yue Raed Al Kontar Ana María Estrada Gómez FedML 31 12 0 15 Jun 2022
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays Konstantin Mishchenko Francis R. Bach Mathieu Even Blake E. Woodworth 23 59 0 15 Jun 2022
Anchor Sampling for Federated Learning with Partial Client Participation Feijie Wu Song Guo Zhihao Qu Shiqi He Ziming Liu Jing Gao FedML 36 12 0 13 Jun 2022
Neurotoxin: Durable Backdoors in Federated Learning Zhengming Zhang Ashwinee Panda Linyue Song Yaoqing Yang Michael W. Mahoney Joseph E. Gonzalez Kannan Ramchandran Prateek Mittal FedML 40 130 0 12 Jun 2022
On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond Xiao-Tong Yuan P. Li FedML 20 59 0 10 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 34 38 0 09 Jun 2022
Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression Xinmeng Huang Yiming Chen W. Yin Kun Yuan 30 25 0 08 Jun 2022
Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation Rustem Islamov Xun Qian Slavomír Hanzely M. Safaryan Peter Richtárik 40 16 0 07 Jun 2022
Pretrained Models for Multilingual Federated Learning Orion Weller Marc Marone Vladimir Braverman Dawn J Lawrie Benjamin Van Durme VLM FedML AI4CE 46 42 0 06 Jun 2022
Straggler-Resilient Personalized Federated Learning Isidoros Tziotis Zebang Shen Ramtin Pedarsani Hamed Hassani Aryan Mokhtari FedML 39 9 0 05 Jun 2022
Communication-Efficient Distributionally Robust Decentralized Learning Matteo Zecchin Marios Kountouris David Gesbert 25 9 0 31 May 2022
Optimal Gradient Sliding and its Application to Distributed Optimization Under Similarity D. Kovalev Aleksandr Beznosikov Ekaterina Borodich Alexander Gasnikov G. Scutari 36 12 0 30 May 2022
A principled framework for the design and analysis of token algorithms Hadrien Hendrikx FedML 29 13 0 30 May 2022
Maximizing Global Model Appeal in Federated Learning Yae Jee Cho Divyansh Jhunjhunwala Tian Li Virginia Smith Gauri Joshi FedML 11 7 0 30 May 2022
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning Liam Collins Hamed Hassani Aryan Mokhtari Sanjay Shakkottai FedML 34 75 0 27 May 2022
Combating Client Dropout in Federated Learning via Friend Model Substitution Heqiang Wang Jie Xu FedML 25 5 0 26 May 2022
Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis Maciej Besta Torsten Hoefler GNN 36 56 0 19 May 2022
Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints Mónica Ribero H. Vikalo G. Veciana FedML 24 44 0 13 May 2022
A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks Mingrui Liu Zhenxun Zhuang Yunwei Lei Chunyang Liao 38 17 0 10 May 2022
Federated Random Reshuffling with Compression and Variance Reduction Grigory Malinovsky Peter Richtárik FedML 29 10 0 08 May 2022
Communication-Efficient Adaptive Federated Learning Yujia Wang Lu Lin Jinghui Chen FedML 27 71 0 05 May 2022
FedNest: Federated Bilevel, Minimax, and Compositional Optimization Davoud Ataee Tarzanagh Mingchen Li Christos Thrampoulidis Samet Oymak FedML 44 73 0 04 May 2022
FedGiA: An Efficient Hybrid Algorithm for Federated Learning Shenglong Zhou Geoffrey Ye Li FedML 36 16 0 03 May 2022
Personalized Federated Learning with Multiple Known Clusters Boxiang Lyu Filip Hanzely Mladen Kolar FedML 30 3 0 28 Apr 2022
On the Convergence of Momentum-Based Algorithms for Federated Bilevel Optimization Problems Hongchang Gao FedML 41 1 0 28 Apr 2022