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Distributed Mean Estimation with Limited Communication

2 November 2016

Sanjiv Kumar

Papers citing "Distributed Mean Estimation with Limited Communication"

50 / 196 papers shown

Title
Quantize Once, Train Fast: Allreduce-Compatible Compression with Provable Guarantees Jihao Xin Marco Canini Peter Richtárik Samuel Horváth 248 2 0 29 May 2023
Private and Communication-Efficient Algorithms for Entropy EstimationNeural Information Processing Systems (NeurIPS), 2023 Gecia Bravo Hermsdorff R. Busa-Fekete Mohammad Ghavamzadeh Andrés Munoz Medina Umar Syed 135 3 0 12 May 2023
Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean EstimationNeural Information Processing Systems (NeurIPS), 2023 Wei-Ning Chen Danni Song Ayfer Özgür Peter Kairouz FedML 179 30 0 04 Apr 2023
Collaborative Mean Estimation over Intermittently Connected Networks with Peer-To-Peer PrivacyInternational Symposium on Information Theory (ISIT), 2023 R. Saha Mohamed Seif M. Yemini Andrea J. Goldsmith H. Vincent Poor FedML 229 2 0 28 Feb 2023
Multi-Message Shuffled Privacy in Federated LearningIEEE Journal on Selected Areas in Information Theory (JSAIT), 2023 Antonious M. Girgis Suhas Diggavi FedML 205 10 0 22 Feb 2023
Breaking the Communication-Privacy-Accuracy Tradeoff with $f$ -Differential PrivacyNeural Information Processing Systems (NeurIPS), 2023 Richeng Jin Z. Su C. Zhong Zhaoyang Zhang Tony Q.S. Quek H. Dai FedML 241 3 0 19 Feb 2023
THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic CompressionSymposium on Networked Systems Design and Implementation (NSDI), 2023 Minghao Li Ran Ben-Basat S. Vargaftik Chon-In Lao Ke Xu Michael Mitzenmacher Minlan Yu Harvard University 246 25 0 16 Feb 2023
DoCoFL: Downlink Compression for Cross-Device Federated LearningInternational Conference on Machine Learning (ICML), 2023 Ron Dorfman S. Vargaftik Y. Ben-Itzhak Kfir Y. Levy FedML 245 33 0 01 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 209 13 0 28 Jan 2023
Distributed Linear Bandits under Communication ConstraintsInternational Conference on Machine Learning (ICML), 2022 Sudeep Salgia Qing Zhao 190 7 0 04 Nov 2022
Adaptive Compression for Communication-Efficient Distributed Training Maksim Makarenko Elnur Gasanov Rustem Islamov Abdurakhmon Sadiev Peter Richtárik 243 18 0 31 Oct 2022
Addressing Heterogeneity in Federated Learning via Distributional TransformationEuropean Conference on Computer Vision (ECCV), 2022 Haolin Yuan Bo Hui Yuchen Yang Philippe Burlina Neil Zhenqiang Gong Yinzhi Cao FedML OOD 128 14 0 26 Oct 2022
Federated Learning and Meta Learning: Approaches, Applications, and DirectionsIEEE Communications Surveys and Tutorials (COMST), 2022 Xiaonan Liu Yansha Deng Arumugam Nallanathan M. Bennis 309 57 0 24 Oct 2022
ScionFL: Efficient and Robust Secure Quantized Aggregation Y. Ben-Itzhak Helen Mollering Benny Pinkas T. Schneider Ajith Suresh Oleksandr Tkachenko S. Vargaftik Christian Weinert Hossein Yalame Avishay Yanai 185 11 0 13 Oct 2022
Sparse Random Networks for Communication-Efficient Federated LearningInternational Conference on Learning Representations (ICLR), 2022 Berivan Isik Francesco Pase Deniz Gunduz Tsachy Weissman M. Zorzi FedML 183 60 0 30 Sep 2022
Personalized Federated Learning with Communication Compression El Houcine Bergou Konstantin Burlachenko Aritra Dutta Peter Richtárik FedML 190 10 0 12 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 156 12 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
Towards Energy-Aware Federated Learning on Battery-Powered Clients Amna Arouj A. Abdelmoniem 148 35 0 09 Aug 2022
Towards Efficient Communications in Federated Learning: A Contemporary SurveyJournal of the Franklin Institute (JFI), 2022 Zihao Zhao Yuzhu Mao Yang Liu Linqi Song Ouyang Ye Xinlei Chen Wenbo Ding FedML 256 68 0 02 Aug 2022
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent KernelsNeural Information Processing Systems (NeurIPS), 2022 Yaodong Yu Alexander Wei Sai Praneeth Karimireddy Yi-An Ma Michael I. Jordan FedML 192 32 0 13 Jul 2022
The Poisson binomial mechanism for secure and private federated learning Wei-Ning Chen Ayfer Özgür Peter Kairouz FedML 96 3 0 09 Jul 2022
sqSGD: Locally Private and Communication Efficient Federated Learning Yan Feng Tao Xiong Ruofan Wu Lingjuan Lv Leilei Shi FedML 162 2 0 21 Jun 2022
SoteriaFL: A Unified Framework for Private Federated Learning with Communication CompressionNeural Information Processing Systems (NeurIPS), 2022 Zhize Li Haoyu Zhao Boyue Li Yuejie Chi FedML 192 47 0 20 Jun 2022
Two-terminal source coding with common sum reconstructionInternational Symposium on Information Theory (ISIT), 2022 Tharindu B. Adikari S. Draper 34 4 0 14 Jun 2022
On the Generalization of Wasserstein Robust Federated Learning Tung Nguyen Tuan Dung Nguyen Long Tan Le Canh T. Dinh N. H. Tran OOD FedML 157 9 0 03 Jun 2022
Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top Eduard A. Gorbunov Samuel Horváth Peter Richtárik Gauthier Gidel AAML 231 0 0 01 Jun 2022
QUIC-FL: Quick Unbiased Compression for Federated Learning Ran Ben-Basat S. Vargaftik Amit Portnoy Gil Einziger Y. Ben-Itzhak Michael Mitzenmacher FedML 289 14 0 26 May 2022
Federated Learning Under Intermittent Client Availability and Time-Varying Communication ConstraintsIEEE Journal on Selected Topics in Signal Processing (IEEE JSTSP), 2022 Mónica Ribero H. Vikalo G. Veciana FedML 125 54 0 13 May 2022
FedShuffle: Recipes for Better Use of Local Work in Federated Learning Samuel Horváth Maziar Sanjabi Lin Xiao Peter Richtárik Michael G. Rabbat FedML 217 22 0 27 Apr 2022
FedSynth: Gradient Compression via Synthetic Data in Federated Learning Shengyuan Hu Jack Goetz Kshitiz Malik Hongyuan Zhan Zhe Liu Yue Liu DD FedML 177 44 0 04 Apr 2022
Scaling Language Model Size in Cross-Device Federated Learning Jae Hun Ro Theresa Breiner Lara McConnaughey Mingqing Chen A. Suresh Shankar Kumar Rajiv Mathews FedML 129 33 0 31 Mar 2022
Correlated quantization for distributed mean estimation and optimizationInternational Conference on Machine Learning (ICML), 2022 A. Suresh Ziteng Sun Jae Hun Ro Felix X. Yu 233 15 0 09 Mar 2022
The Fundamental Price of Secure Aggregation in Differentially Private Federated LearningInternational Conference on Machine Learning (ICML), 2022 Wei-Ning Chen Christopher A. Choquette-Choo Peter Kairouz A. Suresh FedML 214 74 0 07 Mar 2022
Distributed Riemannian Optimization with Lazy Communication for Collaborative Geometric EstimationIEEE/RJS International Conference on Intelligent RObots and Systems (IROS), 2022 Yulun Tian Amrit Singh Bedi Alec Koppel Miguel Calvo-Fullana David M. Rosen Jonathan P. How 191 7 0 02 Mar 2022
Towards Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture SearchIEEE Internet of Things Journal (IEEE IoT J.), 2022 Chunhui Zhang Xiaoming Yuan Qianyun Zhang Guangxu Zhu Lei Cheng Ning Zhang FedML OOD 163 18 0 23 Feb 2022
FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations Jinhyun So Kevin Hsieh Behnaz Arzani Shadi Noghabi Salman Avestimehr Ranveer Chandra FedML 141 73 0 02 Feb 2022
FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients Jianyu Wang Qi A. S. Rawat Sashank J. Reddi Sagar M. Waghmare Felix X. Yu Gauri Joshi FedML 230 29 0 28 Jan 2022
Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory Nicole Mitchell Johannes Ballé Zachary B. Charles Jakub Konecný FedML 257 25 0 07 Jan 2022
Optimal Rate Adaption in Federated Learning with Compressed Communications Laizhong Cui Xiaoxin Su Yipeng Zhou Jiangchuan Liu FedML 149 52 0 13 Dec 2021
Wyner-Ziv Gradient Compression for Federated Learning Kai Liang Huiru Zhong Haoning Chen Youlong Wu FedML 101 8 0 16 Nov 2021
Unified Group Fairness on Federated Learning Tai-wei Chang Kun Kuang Yuxuan Liu Long Chen Chao-Xiang Wu Leilei Gan Jiaxun Lu Yunfeng Shao Jun Xiao FedML 323 20 0 09 Nov 2021
Optimal Compression of Locally Differentially Private MechanismsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2021 Abhin Shah Wei-Ning Chen Johannes Ballé Peter Kairouz Lucas Theis 216 49 0 29 Oct 2021
Distributed Principal Component Analysis with Limited Communication Foivos Alimisis Peter Davies Bart Vandereycken Dan Alistarh 152 15 0 27 Oct 2021
Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation Divyansh Jhunjhunwala Ankur Mallick Advait Gadhikar S. Kadhe Gauri Joshi 153 12 0 14 Oct 2021
FedDQ: Communication-Efficient Federated Learning with Descending Quantization Linping Qu Shenghui Song Chi-Ying Tsui FedML MQ 383 32 0 05 Oct 2021
Communication-Efficient Federated Learning with Binary Neural Networks YuZhi Yang Zhaoyang Zhang Qianqian Yang FedML 135 36 0 05 Oct 2021
Solon: Communication-efficient Byzantine-resilient Distributed Training via Redundant Gradients Lingjiao Chen Leshang Chen Hongyi Wang S. Davidson Guang Cheng FedML 186 1 0 04 Oct 2021
SDR: Efficient Neural Re-ranking using Succinct Document Representation Nachshon Cohen Amit Portnoy B. Fetahu A. Ingber AI4TS 190 11 0 03 Oct 2021
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single MessageInternational Conference on Machine Learning (ICML), 2021 Badih Ghazi Ravi Kumar Pasin Manurangsi Rasmus Pagh Amer Sinha FedML 223 40 0 27 Sep 2021