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Federated learning (FL) enables distribution of machine learning workloads from the cloud to resource-limited edge devices.
Sparse Networks from Scratch: Faster Training without Losing Performance
Dettmers, T.; and Zettlemoyer, L. 2019 · 1907
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Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
Hsu, T.-M. H.; Qi, H.; and Brown, M. 2019 · 1909
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Model Pruning Enables Efficient Federated Learning on Edge Devices
Jiang, Y.; Wang, S.; Valls, V.; Ko, B. J.; Lee, W.-H.; Leung, K. K.; and Tassiulas, L. 2020 · 1909
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Advances and Open Problems in Federated Learning
Kairouz, P.; McMahan, H. B.; Avent, B.; Bellet, A.; Bennis, M.; Bhagoji, A. N.; Bonawitz, K.; Charles, Z.; Cormode, G.; Cummings, R.; D’Oliveira, R. G. L.; Rouayheb, S. E.; Evans, D.; Gardner, J.; Garrett, Z.; Gascón, A.; Ghazi, B.; Gibbons, P. B.; Gruteser, M.; Harchaoui, Z.; He, C.; He, L.; Huo, Z.; Hutchinson, B.; Hsu, J.; Jaggi, M.; Javidi, T.; Joshi, G.; Khodak, M.; Konečný, J.; Korolova, A.; Koushanfar, F.; Koyejo, S.; Lepoint, T.; Liu, Y.; Mittal, P.; Mohri, M.; Nock, R.; Özgür, A.; Pagh, R.; Raykova, M.; Qi, H.; Ramage, D.; Raskar, R.; Song, D.; Song, W.; Stich, S. U.; Sun, Z.; Suresh, A. T.; Tramèr, F.; Vepakomma, P.; Wang, J.; Xiong, L.; Xu, Z.; Yang, Q.; Yu, F. X.; Yu, H.; and Zhao, S. 2019 · 1912
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Optimal Brain Damage
LeCun, Y.; Denker, J.; and Solla, S. 1990 · 1990
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Optimal Brain Surgeon: Extensions and Performance Comparisons
Hassibi, B.; Stork, D. G.; Wolff, G.; and Watanabe, T. 1993 · 1993
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Think locally, act globally: Federated learning with local and global representations
Liang, P. P.; Liu, T.; Ziyin, L.; Salakhutdinov, R.; and Morency, L.-P. 2020 · 2001
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Communication-efficient federated learning via optimal client sampling
Ribero, M.; and Vikalo, H. 2020 · 2007
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Li, A.; Sun, J.; Wang, B.; Duan, L.; Li, S.; Chen, Y.; and Li, H. 2020 · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A. 2009 · 2009
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MNIST handwritten digit database
LeCun, Y.; Cortes, C.; and Burges, C. 2010 · 2010
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Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E.; Srivastava, N.; Krizhevsky, A.; Sutskever, I.; and Salakhutdinov, R. R. 2012 · 2012
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Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
Han, S.; Mao, H.; and Dally, W. J. 2015 · 2015
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Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures
Hu, H.; Peng, R.; Tai, Y.-W.; and Tang, C.-K. 2016 · 2016
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Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon
Dong, X.; Chen, S.; and Pan, S. 2017 · 2017
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Federated Learning: Strategies for Improving Communication Efficiency
Konečný, J.; McMahan, H. B.; Yu, F. X.; Richtárik, P.; Suresh, A. T.; and Bacon, D. 2017 · 2017
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Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, B.; Moore, E.; Ramage, D.; Hampson, S.; and y Arcas, B. A. 2017 · 2017
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Pruning Convolutional Neural Networks for Resource Efficient Inference
Molchanov, P.; Tyree, S.; Karras, T.; Aila, T.; and Kautz, J. 2017 · 2017
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Rigging the lottery: Making all tickets winners
Evci, U.; Gale, T.; Menick, J.; Castro, P. S.; and Elsen, E. 2020 · 2020
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Linear mode connectivity and the lottery ticket hypothesis
Frankle, J.; Dziugaite, G. K.; Roy, D.; and Carbin, M. 2020 · 2020
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Picking Winning Tickets Before Training by Preserving Gradient Flow
Wang, C.; Zhang, G.; and Grosse, R. 2020 · 2020
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Federated Learning with Matched Averaging
Wang, H.; Yurochkin, M.; Sun, Y.; Papailiopoulos, D.; and Khazaeni, Y. 2020 · 2020
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Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks
You, H.; Li, C.; Xu, P.; Fu, Y.; Wang, Y.; Chen, X.; Baraniuk, R. G.; Wang, Z.; and Lin, Y. 2020 · 2020
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The lottery tickets hypothesis for supervised and self-supervised pre-training in computer vision models
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The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Frankle, J.; and Carbin, M. 2018 · 2018
Cited alongside, same era.
Federated optimization in heterogeneous networks
Li, T.; Sahu, A. K.; Zaheer, M.; Sanjabi, M.; Talwalkar, A.; and Smith, V. 2018 · 2018
Cited alongside, same era.
Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
Mocanu, D. C.; Mocanu, E.; Stone, P.; Nguyen, P. H.; Gibescu, M.; and Liotta, A. 2018 · 2018
Cited alongside, same era.
The state of sparsity in deep neural networks
Gale, T.; Elsen, E.; and Hooker, S. 2019 · 2019
Cited alongside, same era.
SNIP: Single-shot network pruning based on connection sensitivity
Lee, N.; Ajanthan, T.; and Torr, P. 2019 · 2019
Cited alongside, same era.
The lottery ticket hypothesis for pre-trained bert networks
Chen, T.; Frankle, J.; Chang, S.; Liu, S.; Zhang, Y.; Wang, Z.; and Carbin, M. 2020 · 2020
Cited alongside, same era.
Fast sparse convnets
Elsen, E.; Dukhan, M.; Gale, T.; and Simonyan, K. 2020 · 2020
Cited alongside, same era.
Chen, T.; Frankle, J.; Chang, S.; Liu, S.; Zhang, Y.; Carbin, M.; and Wang, Z. 2021 · 2021
Closest in time.
Federated Adversarial Debiasing for Fair and Transferable Representations
Hong, J.; Zhu, Z.; Yu, S.; Wang, Z.; Dodge, H. H.; and Zhou, J. 2021 · 2021
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Model-Contrastive Federated Learning
Li, Q.; He, B.; and Song, D. 2021 · 2021
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Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?
Ma, X.; Yuan, G.; Shen, X.; Chen, T.; Chen, X.; Chen, X.; Liu, N.; Qin, M.; Liu, S.; Wang, Z.; et al. 2021 · 2021
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Adaptive Federated Optimization
Reddi, S. J.; Charles, Z.; Zaheer, M.; Garrett, Z.; Rush, K.; Konečný, J.; Kumar, S.; and McMahan, H. B. 2021 · 2021
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Addressing Class Imbalance in Federated Learning
Wang, L.; Xu, S.; Wang, X.; and Zhu, Q. 2021 · 2021
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Learning structured sparsity in deep neural networks
Wen, W.; Wu, C.; Wang, Y.; Chen, Y.; and Li, H. 2016 · 2090
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