Fetching the paper…
Reading the bibliography…
Devices participating in federated learning (FL) typically have heterogeneous communication, computation, and memory resources.
A survey of on-device machine learning: An algorithms and learning theory perspective
Sauptik Dhar, Junyao Guo, Jiayi (Jason) Liu, Samarth Tripathi, Unmesh Kurup, and Mohak Shah · 1914
Earlier work this paper cites.
Wireless Communications
Andrea Goldsmith · 2005
Earlier work this paper cites.
Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
Earlier work this paper cites.
Deep learning with limited numerical precision
Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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.
Emnist: an extension of mnist to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and André van Schaik · 2017
Earlier work this paper cites.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
Earlier work this paper cites.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q. Weinberger · 2017
Earlier work this paper cites.
Training quantized nets: A deeper understanding
Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, and Tom Goldstein · 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.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M. Summers · 2017
Earlier work this paper cites.
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.
A survey on methods and theories of quantized neural networks
Yunhui Guo · 2018
Cited alongside, same era.
Quantization and training of neural networks for efficient integer-arithmetic-only inference
Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko · 2018
Cited alongside, same era.
Mixed precision training
Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, and Hao Wu · 2018
Cited alongside, same era.
Group normalization
Yuxin Wu and Kaiming He · 2018
Cited alongside, same era.
Layerout: Freezing layers in deep neural networks
Kelam Goutam, S Balasubramanian, Darshan Gera, and R Raghunatha Sarma · 2020
Later among the works it cites.
Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
Later among the works it cites.
Communication-Efficient Edge AI: Algorithms and Systems
Yuanming Shi, Kai Yang, Tao Jiang, Jun Zhang, and Khaled B Letaief · 2020
Later among the works it cites.
Asynchronous federated optimization
Cong Xie, Sanmi Koyejo, and Indranil Gupta · 2020
Later among the works it cites.
Energy efficient federated learning over wireless communication networks
Zhaohui Yang, Mingzhe Chen, Walid Saad, Choong Seon Hong, and Mohammad Shikh-Bahaei · 2020
Later among the works it cites.
Communication-efficient federated learning with adaptive parameter freezing
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, and Françoise Beaufays · 2018
Cited alongside, same era.
Recent trends in deep learning based natural language processing
Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria · 2018
Cited alongside, same era.
Leaf: A benchmark for federated settings
S Caldas, P Wu, T Li, J Konecnỳ, HB McMahan, V Smith, and A Talwalkar · 2019
Cited alongside, same era.
Measuring the effects of non-identical data distribution for federated visual classification
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 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.
Compressing RNNs for IoT Devices by 15-38x using Kronecker Products
Urmish Thakker, Jesse Beu, Dibakar Gope, Chu Zhou, Igor Fedorov, Ganesh Dasika, and Matthew Mattina · 2019
Cited alongside, same era.
New directions in distributed deep learning: Bringing the network at forefront of iot design
Kartikeya Bhardwaj, Wei Chen, and Radu Marculescu · 2020
Cited alongside, same era.
Chen Chen, Hong Xu, Wei Wang, Baochun Li, Bo Li, Li Chen, and Gong Zhang · 2021
Later among the works it cites.
Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout
Samuel Horvath, Stefanos Laskaridis, Mario Almeida, Ilias Leontiadis, Stylianos Venieris, and Nicholas Lane · 2021
Later among the works it cites.
A survey of fairness-aware federated learning
Yuxin Shi, Han Yu, and Cyril Leung · 2021
Later among the works it cites.
Helios: Heterogeneity-aware federated learning with dynamically balanced collaboration
Zirui Xu, Fuxun Yu, Jinjun Xiong, and Xiang Chen · 2021
Later among the works it cites.
Towards fair federated recommendation learning: Characterizing the inter-dependence of system and data heterogeneity
Kiwan Maeng, Haiyu Lu, Luca Melis, John Nguyen, Mike Rabbat, and Carole-Jean Wu · 2022
Closest in time.
Distreal: Distributed resource-aware learning in heterogeneous systems
Martin Rapp, Ramin Khalili, Kilian Pfeiffer, and Jörg Henkel · 2022
Closest in time.
Scaling language model size in cross-device federated learning
Jae Ro, Theresa Breiner, Lara McConnaughey, Mingqing Chen, Ananda Suresh, Shankar Kumar, and Rajiv Mathews · 2022
Closest in time.
Partial variable training for efficient on-device federated learning
Tien-Ju Yang, Dhruv Guliani, Françoise Beaufays, and Giovanni Motta · 2022
Closest in time.