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Adaptive Compression in Federated Learning via Side Information

22 June 2023

Deniz Gunduz

Papers citing "Adaptive Compression in Federated Learning via Side Information"

23 / 23 papers shown

Title
QUIC-FL: Quick Unbiased Compression for Federated Learning Ran Ben-Basat S. Vargaftik Amit Portnoy Gil Einziger Y. Ben-Itzhak Michael Mitzenmacher FedML 124 13 0 26 May 2022
DP-REC: Private & Communication-Efficient Federated Learning Aleksei Triastcyn M. Reisser Christos Louizos FedML 57 16 0 09 Nov 2021
Optimal Compression of Locally Differentially Private Mechanisms Abhin Shah Wei-Ning Chen Johannes Ballé Peter Kairouz Lucas Theis 73 42 0 29 Oct 2021
EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning S. Vargaftik Ran Ben-Basat Amit Portnoy Gal Mendelson Y. Ben-Itzhak Michael Mitzenmacher FedML 93 49 0 19 Aug 2021
Masked Training of Neural Networks with Partial Gradients Amirkeivan Mohtashami Martin Jaggi Sebastian U. Stich 105 23 0 16 Jun 2021
QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning Maxime Vono Vincent Plassier Alain Durmus Aymeric Dieuleveut Eric Moulines FedML 70 36 0 01 Jun 2021
An Information-Theoretic Justification for Model Pruning Berivan Isik Tsachy Weissman Albert No 138 37 0 16 Feb 2021
MARINA: Faster Non-Convex Distributed Learning with Compression Eduard A. Gorbunov Konstantin Burlachenko Zhize Li Peter Richtárik 96 110 0 15 Feb 2021
Time-Correlated Sparsification for Communication-Efficient Federated Learning Emre Ozfatura Kerem Ozfatura Deniz Gunduz FedML 86 49 0 21 Jan 2021
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning Hong-You Chen Wei-Lun Chao FedML 80 262 0 04 Sep 2020
LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets Ang Li Jingwei Sun Binghui Wang Lin Duan Sicheng Li Yiran Chen H. Li FedML 82 127 0 07 Aug 2020
FetchSGD: Communication-Efficient Federated Learning with Sketching D. Rothchild Ashwinee Panda Enayat Ullah Nikita Ivkin Ion Stoica Vladimir Braverman Joseph E. Gonzalez Raman Arora FedML 95 370 0 15 Jul 2020
rTop-k: A Statistical Estimation Approach to Distributed SGD L. P. Barnes Huseyin A. Inan Berivan Isik Ayfer Özgür 68 65 0 21 May 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 275 6,294 0 10 Dec 2019
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization Thijs Vogels Sai Praneeth Karimireddy Martin Jaggi 99 322 0 31 May 2019
LEAF: A Benchmark for Federated Settings S. Caldas Sai Meher Karthik Duddu Peter Wu Tian Li Jakub Konecný H. B. McMahan Virginia Smith Ameet Talwalkar FedML 158 1,424 0 03 Dec 2018
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters Marton Havasi Robert Peharz José Miguel Hernández-Lobato 78 82 0 30 Sep 2018
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training Chengyue Wu Song Han Huizi Mao Yu Wang W. Dally 152 1,410 0 05 Dec 2017
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning W. Wen Cong Xu Feng Yan Chunpeng Wu Yandan Wang Yiran Chen Hai Helen Li 184 990 0 22 May 2017
Sparse Communication for Distributed Gradient Descent Alham Fikri Aji Kenneth Heafield 89 742 0 17 Apr 2017
Distributed Mean Estimation with Limited Communication A. Suresh Felix X. Yu Sanjiv Kumar H. B. McMahan FedML 133 366 0 02 Nov 2016
Federated Learning: Strategies for Improving Communication Efficiency Jakub Konecný H. B. McMahan Felix X. Yu Peter Richtárik A. Suresh Dave Bacon FedML 312 4,659 0 18 Oct 2016
The sample size required in importance sampling S. Chatterjee P. Diaconis 149 191 0 04 Nov 2015