Fetching the paper…
Reading the bibliography…
In Federated Learning, a common approach for aggregating local models across clients is periodic averaging of the full model parameters.
Measuring statistical dependence with hilbert-schmidt norms
Arthur Gretton, Olivier Bousquet, Alex Smola, and Bernhard Schölkopf · 2005
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.
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.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Sergey Zagoruyko and Nikos Komodakis · 2016
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.
Gradient sparsification for communication-efficient distributed optimization
Jianqiao Wangni, Jialei Wang, Ji Liu, and Tong Zhang · 2017
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.
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.
Emnist: Extending mnist to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and Andre Van Schaik · 2017
Earlier work this paper cites.
Maithra Raghu, Justin Gilmer, Jason Yosinski, and Jascha Sohl-Dickstein · 2017
Earlier work this paper cites.
Freezeout: Accelerate training by progressively freezing layers
Andrew Brock, Theodore Lim, James M Ritchie, and Nick Weston · 2017
Cited alongside, same era.
Accurate, large minibatch sgd: Training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
Cited alongside, same era.
Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2018
Cited alongside, same era.
Adaptive communication strategies to achieve the best error-runtime trade-off in local-update sgd
Jianyu Wang and Gauri Joshi · 2018
Cited alongside, same era.
Accelerating deep learning inference via freezing
Adarsh Kumar, Arjun Balasubramanian, Shivaram Venkataraman, and Aditya Akella · 2019
Later among the works it cites.
Tackling the objective inconsistency problem in heterogeneous federated optimization
Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H Vincent Poor · 2020
Later among the works it cites.
Scaffold: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
Later among the works it cites.
Optimal gradient compression for distributed and federated learning
Alyazeed Albasyoni, Mher Safaryan, Laurent Condat, and Peter Richtárik · 2020
Later among the works it cites.
Practical low-rank communication compression in decentralized deep learning
Thijs Vogels, Sai Praneeth Karimireddy, and Martin Jaggi · 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…
Dan Alistarh, Torsten Hoefler, Mikael Johansson, Sarit Khirirat, Nikola Konstantinov, and Cédric Renggli · 2018
Cited alongside, same era.
Atomo: Communication-efficient learning via atomic sparsification
Hongyi Wang, Scott Sievert, Zachary Charles, Shengchao Liu, Stephen Wright, and Dimitris Papailiopoulos · 2018
Cited alongside, same era.
Insights on representational similarity in neural networks with canonical correlation
Ari S Morcos, Maithra Raghu, and Samy Bengio · 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.
Large batch optimization for deep learning: Training bert in 76 minutes
Yang You, Jing Li, Sashank Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, and Cho-Jui Hsieh · 2019
Cited alongside, same era.
Local sgd with periodic averaging: Tighter analysis and adaptive synchronization
Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi, and Viveck R Cadambe · 2019
Cited alongside, same era.
Similarity of neural network representations revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton · 2019
Cited alongside, same era.
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.
Heterofl: Computation and communication efficient federated learning for heterogeneous clients
Enmao Diao, Jie Ding, and Vahid Tarokh · 2020
Later among the works it cites.
Accelerating training of transformer-based language models with progressive layer dropping
Minjia Zhang and Yuxiong He · 2020
Later among the works it cites.
Layerout: Freezing layers in deep neural networks
Kelam Goutam, S Balasubramanian, Darshan Gera, and R Raghunatha Sarma · 2020
Later among the works it cites.
Fedml: A research library and benchmark for federated machine learning
Chaoyang He, Songze Li, Jinhyun So, Xiao Zeng, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, et al · 2020
Later among the works it cites.
Pufferfish: Communication-efficient models at no extra cost
Hongyi Wang, Saurabh Agarwal, and Dimitris Papailiopoulos · 2021
Closest in time.