Fetching the paper…
Reading the bibliography…
In this work, we propose a communication-efficient parameterization, FedPara, for federated learning (FL) to overcome the burdens on frequent model uploads and downloads.
The complete works of william shakespeare
William Shakespeare · 1994
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Speeding-up convolutional neural networks using fine-tuned cp-decomposition
Vadim Lebedev, Yaroslav Ganin, Maksim Rakhuba, Ivan V. Oseledets, and Victor S. Lempitsky · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Earlier work this paper cites.
Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
Earlier work this paper cites.
Convolutional neural networks with low-rank regularization
Cheng Tai, Tong Xiao, Xiaogang Wang, and Weinan E · 2016
Earlier work this paper cites.
A review on energy efficient protocols in wireless sensor networks
Sarika Yadav and Rama Shankar Yadav · 2016
Earlier work this paper cites.
Qsgd: Communication-efficient sgd via gradient quantization and encoding
Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic · 2017
Earlier work this paper cites.
Thinet: A filter level pruning method for deep neural network compression
Jian-Hao Luo, Jianxin Wu, and Weiyao Lin · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Earlier work this paper cites.
Introduction to tensor decompositions and their applications in machine learning
Stephan Rabanser, Oleksandr Shchur, and Stephan Günnemann · 2017
Earlier work this paper cites.
Terngrad: Ternary gradients to reduce communication in distributed deep learning
Wei Wen, Cong Xu, Feng Yan, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2017
Earlier work this paper cites.
The convergence of sparsified gradient methods
Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat, and Cedric Renggli · 2018
Earlier work this paper cites.
signSGD: Compressed optimisation for non-convex problems
Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, and Animashree Anandkumar · 2018
Cited alongside, same era.
Leaf: A benchmark for federated settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečnỳ, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
Cited alongside, same era.
Cinic-10 is not imagenet or cifar-10
Luke N Darlow, Elliot J Crowley, Antreas Antoniou, and Amos J Storkey · 2018
Cited alongside, same era.
Deep gradient compression: Reducing the communication bandwidth for distributed training
Yujun Lin, Song Han, Huizi Mao, Yu Wang, and Bill Dally · 2018
Cited alongside, same era.
Federated learning with personalization layers
Manoj Ghuhan Arivazhagan, Vinay Aggarwal, Aaditya Kumar Singh, and Sunav Choudhary · 2019
Cited alongside, same era.
Stable low-rank tensor decomposition for compression of convolutional neural network
Anh-Huy Phan, Konstantin Sobolev, Konstantin Sozykin, Dmitry Ermilov, Julia Gusak, Petr Tichavskỳ, Valeriy Glukhov, Ivan Oseledets, and Andrzej Cichocki · 2020
Later among the works it cites.
Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization
Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie, and Ramtin Pedarsani · 2020
Later among the works it cites.
A survey of privacy attacks in machine learning
Maria Rigaki and Sebastian Garcia · 2020
Later among the works it cites.
Implicit neural representations with periodic activation functions
Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein · 2020
Later among the works it cites.
Federated learning with only positive labels
Felix Yu, Ankit Singh Rawat, Aditya Menon, and Sanjiv Kumar · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
Cited alongside, same era.
Efficient neural network compression
Hyeji Kim, Muhammad Umar Karim Khan, and Chong-Min Kyung · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Cited alongside, same era.
Powersgd: Practical low-rank gradient compression for distributed optimization
Thijs Vogels, Sai Praneeth Karimireddy, and Martin Jaggi · 2019
Cited alongside, same era.
Modeling the total energy consumption of mobile network services and applications
Ming Yan, Chien Aun Chan, André F Gygax, Jinyao Yan, Leith Campbell, Ampalavanapillai Nirmalathas, and Christopher Leckie · 2019
Cited alongside, same era.
Batch normalization biases residual blocks towards the identity function in deep networks
Soham De and Sam Smith · 2020
Cited alongside, same era.
The non-iid data quagmire of decentralized machine learning
Kevin Hsieh, Amar Phanishayee, Onur Mutlu, and Phillip Gibbons · 2020
Cited alongside, same era.
Federated accelerated stochastic gradient descent
Honglin Yuan and Tengyu Ma · 2020
Later among the works it cites.
One-bit over-the-air aggregation for communication-efficient federated edge learning: Design and convergence analysis
Guangxu Zhu, Yuqing Du, Deniz Gündüz, and Kaibin Huang · 2020
Later among the works it cites.
Federated learning based on dynamic regularization
Durmus Alp Emre Acar, Yue Zhao, Ramon Matas, Matthew Mattina, Paul Whatmough, and Venkatesh Saligrama · 2021
Closest in time.
Adaptive gradient communication via critical learning regime identification
Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, and Dimitris Papailiopoulos · 2021
Closest in time.
HeteroFL: Computation and communication efficient federated learning for heterogeneous clients
Enmao Diao, Jie Ding, and Vahid Tarokh · 2021
Closest in time.
Federated learning with compression: Unified analysis and sharp guarantees
Farzin Haddadpour, Mohammad Mahdi Kamani, Aryan Mokhtari, and Mehrdad Mahdavi · 2021
Closest in time.
Communication-efficient federated learning with dual-side low-rank compression
Zhefeng Qiao, Xianghao Yu, Jun Zhang, and Khaled B Letaief · 2021
Closest in time.
Adaptive federated optimization
Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, and Hugh Brendan McMahan · 2021
Closest in time.
SPEEDTEST
Speedtest · 2021
Closest in time.
Pufferfish: Communication-efficient models at no extra cost
Hongyi Wang, Saurabh Agarwal, and Dimitris Papailiopoulos · 2021
Closest in time.