Federated Dynamic Sparse Training: Computing Less, Communicating Less,
Yet Learning Better

Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better

18 December 2021

Papers citing "Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better"

18 / 18 papers shown

Title
PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Yisu Wang Ruilong Wu Xinjiao Li Dirk Kutscher 175 0 0 24 May 2025
Enhancing Efficiency in Multidevice Federated Learning through Data Selection Fan Mo Mohammad Malekzadeh S. Chatterjee F. Kawsar Akhil Mathur FedML 61 2 0 08 Nov 2022
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity Shiwei Liu Tianlong Chen Zahra Atashgahi Xiaohan Chen Ghada Sokar Elena Mocanu Mykola Pechenizkiy Zhangyang Wang Decebal Constantin Mocanu OOD 47 52 0 28 Jun 2021
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 57 127 0 07 Aug 2020
Adaptive Federated Optimization Sashank J. Reddi Zachary B. Charles Manzil Zaheer Zachary Garrett Keith Rush Jakub Konecný Sanjiv Kumar H. B. McMahan FedML 92 1,431 0 29 Feb 2020
Think Locally, Act Globally: Federated Learning with Local and Global Representations Paul Pu Liang Terrance Liu Liu Ziyin Nicholas B. Allen Randy P. Auerbach David Brent Ruslan Salakhutdinov Louis-Philippe Morency FedML 106 559 0 06 Jan 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 169 6,226 0 10 Dec 2019
Rigging the Lottery: Making All Tickets Winners Utku Evci Trevor Gale Jacob Menick Pablo Samuel Castro Erich Elsen 160 592 0 25 Nov 2019
Model Pruning Enables Efficient Federated Learning on Edge Devices Yuang Jiang Shiqiang Wang Victor Valls Bongjun Ko Wei-Han Lee Kin K. Leung Leandros Tassiulas 56 461 0 26 Sep 2019
Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification T. Hsu Qi Matthew Brown FedML 121 1,143 0 13 Sep 2019
Sparse Networks from Scratch: Faster Training without Losing Performance Tim Dettmers Luke Zettlemoyer 112 339 0 10 Jul 2019
The State of Sparsity in Deep Neural Networks Trevor Gale Erich Elsen Sara Hooker 123 758 0 25 Feb 2019
SNIP: Single-shot Network Pruning based on Connection Sensitivity Namhoon Lee Thalaiyasingam Ajanthan Philip Torr VLM 225 1,194 0 04 Oct 2018
Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science Decebal Constantin Mocanu Elena Mocanu Peter Stone Phuong H. Nguyen M. Gibescu A. Liotta 146 626 0 15 Jul 2017
Federated Learning: Strategies for Improving Communication Efficiency Jakub Konecný H. B. McMahan Felix X. Yu Peter Richtárik A. Suresh Dave Bacon FedML 282 4,634 0 18 Oct 2016
Learning Structured Sparsity in Deep Neural Networks W. Wen Chunpeng Wu Yandan Wang Yiran Chen Hai Helen Li 146 2,335 0 12 Aug 2016
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding Song Han Huizi Mao W. Dally 3DGS 227 8,821 0 01 Oct 2015
Improving neural networks by preventing co-adaptation of feature detectors Geoffrey E. Hinton Nitish Srivastava A. Krizhevsky Ilya Sutskever Ruslan Salakhutdinov VLM 414 7,657 0 03 Jul 2012

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