v1v2 (latest)

Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations

IEEE Journal on Selected Areas in Information Theory (JSAIT), 2019

6 June 2019

Suhas Diggavi

Papers citing "Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations"

50 / 223 papers shown

Title
Fundamental Limits of Distributed Optimization over Multiple Access ChannelInformation Theory Workshop (ITW), 2023 Shubham K. Jha 175 1 0 05 Oct 2023
Stochastic Controlled Averaging for Federated Learning with Communication CompressionInternational Conference on Learning Representations (ICLR), 2023 Xinmeng Huang Ping Li Xiaoyun Li 339 245 0 16 Aug 2023
Information-Theoretically Private Federated Submodel Learning with Storage Constrained DatabasesIEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2023 Sajani Vithana S. Ulukus FedML 187 1 0 12 Jul 2023
Benchmarking Algorithms for Federated Domain GeneralizationInternational Conference on Learning Representations (ICLR), 2023 Ruqi Bai S. Bagchi David I. Inouye FedML 209 18 0 11 Jul 2023
GQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing SystemsIEEE Transactions on Wireless Communications (IEEE TWC), 2023 Yangchen Li Ying Cui Vincent K. N. Lau FedML 212 4 0 13 Jun 2023
Improving Accelerated Federated Learning with Compression and Importance Sampling Michal Grudzieñ Grigory Malinovsky Peter Richtárik FedML 236 11 0 05 Jun 2023
Federated Multi-Sequence Stochastic Approximation with Local Hypergradient Estimation Davoud Ataee Tarzanagh Mingchen Li Pranay Sharma Samet Oymak 231 0 0 02 Jun 2023
SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel LearningNeural Information Processing Systems (NeurIPS), 2023 Yifan Yang Peiyao Xiao Kaiyi Ji FedML 365 23 0 30 May 2023
Two Results on Low-Rank Heavy-Tailed Multiresponse Regressions Kangqiang Li Yuxuan Wang 189 1 0 23 May 2023
Convergence Analysis of Over-the-Air FL with Compression and Power Control via ClippingGlobal Communications Conference (GLOBECOM), 2023 Haifeng Wen Hong Xing Osvaldo Simeone 210 2 0 18 May 2023
Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression Yutong He Xinmeng Huang Yiming Chen W. Yin Kun Yuan 351 9 0 12 May 2023
FedAVO: Improving Communication Efficiency in Federated Learning with African Vultures OptimizerAnnual International Computer Software and Applications Conference (COMPSAC), 2023 Md Zarif Hossain Ahmed Imteaj FedML 259 7 0 02 May 2023
Killing Two Birds with One Stone: Quantization Achieves Privacy in Distributed Learning Guangfeng Yan Tan Li Kui Wu Linqi Song 165 14 0 26 Apr 2023
Joint Client Assignment and UAV Route Planning for Indirect-Communication Federated Learning Jieming Bian Cong Shen Jie Xu FedML 221 3 0 21 Apr 2023
SparDL: Distributed Deep Learning Training with Efficient Sparse CommunicationIEEE International Conference on Data Engineering (ICDE), 2023 Minjun Zhao Yichen Yin Yuren Mao Qing Liu Lu Chen Yunjun Gao 145 1 0 03 Apr 2023
FedREP: A Byzantine-Robust, Communication-Efficient and Privacy-Preserving Framework for Federated Learning Yi-Rui Yang Kun Wang Wulu Li FedML 218 6 0 09 Mar 2023
Private Read-Update-Write with Controllable Information Leakage for Storage-Efficient Federated Learning with Top $r$ SparsificationIEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2023 Sajani Vithana S. Ulukus FedML 129 7 0 07 Mar 2023
DeAR: Accelerating Distributed Deep Learning with Fine-Grained All-Reduce PipeliningIEEE International Conference on Distributed Computing Systems (ICDCS), 2023 Lin Zhang Shaoshuai Shi Xiaowen Chu Wei Wang Yue Liu Chengjian Liu 157 17 0 24 Feb 2023
Federated Gradient Matching PursuitIEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2023 Halyun Jeong Deanna Needell Jing Qin FedML 144 2 0 20 Feb 2023
TAMUNA: Doubly Accelerated Distributed Optimization with Local Training, Compression, and Partial Participation Laurent Condat Ivan Agarský Grigory Malinovsky Peter Richtárik FedML 295 3 0 20 Feb 2023
Magnitude Matters: Fixing SIGNSGD Through Magnitude-Aware Sparsification in the Presence of Data Heterogeneity Richeng Jin Xiaofan He C. Zhong Zhaoyang Zhang Tony Q.S. Quek H. Dai FedML 154 2 0 19 Feb 2023
Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational InequalitiesNeural Information Processing Systems (NeurIPS), 2023 Aleksandr Beznosikov Martin Takáč Alexander Gasnikov 240 12 0 15 Feb 2023
Sparse-SignSGD with Majority Vote for Communication-Efficient Distributed LearningInternational Symposium on Information Theory (ISIT), 2023 Chanho Park Namyoon Lee FedML 132 6 0 15 Feb 2023
Federated Learning via Indirect Server-Client CommunicationsAnnual Conference on Information Sciences and Systems (CISS), 2023 Jieming Bian Cong Shen Jie Xu FedML 72 5 0 14 Feb 2023
EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous DataInternational Conference on Learning Representations (ICLR), 2023 M. Crawshaw Yajie Bao Mingrui Liu FedML 173 9 0 14 Feb 2023
Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative DifferentiationInternational Conference on Machine Learning (ICML), 2023 Peiyao Xiao Kaiyi Ji FedML 181 16 0 09 Feb 2023
On the Convergence of Federated Averaging with Cyclic Client ParticipationInternational Conference on Machine Learning (ICML), 2023 Yae Jee Cho Pranay Sharma Gauri Joshi Zheng Xu Satyen Kale Tong Zhang FedML 219 41 0 06 Feb 2023
$z$ -SignFedAvg: A Unified Stochastic Sign-based Compression for Federated LearningAAAI Conference on Artificial Intelligence (AAAI), 2023 Zhiwei Tang Yanmeng Wang Tsung-Hui Chang FedML 246 21 0 06 Feb 2023
Does Federated Learning Really Need Backpropagation?European Conference on Computer Vision (ECCV), 2023 Hao Feng Tianyu Pang Chao Du Wei Chen Shuicheng Yan Min Lin FedML 233 12 0 28 Jan 2023
Decentralized Entropic Optimal Transport for Privacy-preserving Distributed Distribution Comparison Xiangfeng Wang Hongteng Xu Moyi Yang OT 277 2 0 28 Jan 2023
Optimus-CC: Efficient Large NLP Model Training with 3D Parallelism Aware Communication CompressionInternational Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2023 Jaeyong Song Jinkyu Yim Jaewon Jung Hongsun Jang H. Kim Youngsok Kim Jinho Lee GNN 238 37 0 24 Jan 2023
ABS: Adaptive Bounded Staleness Converges Faster and Communicates Less Qiao Tan Feng Zhu Jingjing Zhang 237 0 0 21 Jan 2023
A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative RegretIEEE Transactions on Signal Processing (IEEE TSP), 2023 Sudeep Salgia Qing Zhao T. Gabay Kobi Cohen FedML 261 13 0 21 Jan 2023
Network Adaptive Federated Learning: Congestion and Lossy CompressionIEEE Conference on Computer Communications (IEEE INFOCOM), 2023 Parikshit Hegde G. Veciana Aryan Mokhtari FedML 225 8 0 11 Jan 2023
Federated Learning with Flexible ControlIEEE Conference on Computer Communications (INFOCOM), 2022 Maroun Touma Jake B. Perazzone Mingyue Ji Kevin S. Chan FedML 206 20 0 16 Dec 2022
SplitGP: Achieving Both Generalization and Personalization in Federated LearningIEEE Conference on Computer Communications (INFOCOM), 2022 Dong-Jun Han Do-Yeon Kim Minseok Choi Christopher G. Brinton Jaekyun Moon FedML 157 47 0 16 Dec 2022
GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated LearningConference on Machine Learning and Systems (MLSys), 2022 Shiqi He Qifan Yan Feijie Wu Lanjun Wang Mathias Lécuyer Ivan Beschastnikh FedML 194 11 0 03 Dec 2022
Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression Xiaoyun Li Ping Li FedML 184 6 0 25 Nov 2022
Online Federated Learning via Non-Stationary Detection and Adaptation amidst Concept DriftIEEE/ACM Transactions on Networking (TON), 2022 Bhargav Ganguly Vaneet Aggarwal FedML 170 14 0 22 Nov 2022
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and ChallengesIEEE Communications Surveys and Tutorials (COMST), 2022 Enrique Tomás Martínez Beltrán Mario Quiles Pérez Pedro Miguel Sánchez Sánchez Sergio López Bernal Gérome Bovet M. Pérez Gregorio Martínez Pérez Alberto Huertas Celdrán FedML 407 375 0 15 Nov 2022
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism DesignInternational Conference on Machine Learning (ICML), 2022 Chuan Guo Kamalika Chaudhuri Pierre Stock Michael G. Rabbat FedML 265 7 0 08 Nov 2022
A Convergence Theory for Federated Average: Beyond Smoothness Xiaoxiao Li Zhao Song Runzhou Tao Guangyi Zhang FedML 119 5 0 03 Nov 2022
FeDXL: Provable Federated Learning for Deep X-Risk OptimizationInternational Conference on Machine Learning (ICML), 2022 Zhishuai Guo Rong Jin Jiebo Luo Tianbao Yang FedML 386 10 0 26 Oct 2022
Provably Doubly Accelerated Federated Learning: The First Theoretically Successful Combination of Local Training and Communication Compression Laurent Condat Ivan Agarský Peter Richtárik FedML 272 17 0 24 Oct 2022
Personalized Federated Learning with Communication Compression El Houcine Bergou Konstantin Burlachenko Aritra Dutta Peter Richtárik FedML 206 10 0 12 Sep 2022
Private Read Update Write (PRUW) in Federated Submodel Learning (FSL): Communication Efficient Schemes With and Without SparsificationIEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2022 Sajani Vithana S. Ulukus FedML 196 22 0 09 Sep 2022
Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air ComputationIEEE Transactions on Wireless Communications (TWC), 2022 Liang Li Chenpei Huang Dian Shi Hao Wang Xiangwei Zhou Minglei Shu Miao Pan FedML 172 13 0 15 Aug 2022
A Fast Blockchain-based Federated Learning Framework with Compressed CommunicationsIEEE Journal on Selected Areas in Communications (JSAC), 2022 Laizhong Cui Xiaoxin Su Yipeng Zhou FedML 111 32 0 12 Aug 2022
Adaptive Step-Size Methods for Compressed SGDIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2022 Adarsh M. Subramaniam A. Magesh Venugopal V. Veeravalli 112 1 0 20 Jul 2022
Fast Composite Optimization and Statistical Recovery in Federated LearningInternational Conference on Machine Learning (ICML), 2022 Yajie Bao M. Crawshaw Sha Luo Mingrui Liu FedML 187 17 0 17 Jul 2022